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Using the OpenSearch search engine, the Lexbe eDiscovery Platform (LEP) offers comprehensive, multi-index search methods to search for specific documents across an entire case using various search options.

LEP offers search results generated from Optical Character Recognition (OCR), which captures image-based text, as well as native extraction, meaning text that is excluded from print and/or hidden/embedded text. Going a step further, LEP offers an additional index to search across translated documents, using a multi-lingual search index.

Noise Words

Noise words do not exist in OpenSearch. As such, all words are indexed.

Boolean Searches

LEP supports advanced Boolean searches. A Boolean search consists of a group of words or phrases linked by connectors such as AND and OR that indicate relationship or logic. Supported Boolean operators are listed below with examples. Please note, Boolean operators must be capitalized when used.


Example Search Term: Apple AND Pear

Expected Results: Returns documents that only contain both of the words Apple and Pear.


Example Search Term: Apple OR Pear

Expected Results: Returns documents that contain either of the words Apple and Pear.

**A single space can be used in place of the OR operator. In this example, the search term would read: Apple Pear


Example Search Term: Apple NOT Pear

Example Search Term: Apple AND NOT Pear

Expected Results: Returns documents that contain the word Apple but not Pear.

Boolean Search Tips

To use more than one connector, use parentheses to indicate the precise search criteria. For example, apple and pear or orange juice could mean (apple and pear) or orange, or it could mean apple and (pear or orange).

Wildcard Searches

Single and multi character wildcard searches are supported in LEP when searching document content or metadata. Examples of some supported wildcard searches are included below.

Single Character Wildcard: ?

Example Search Term: Appl?

Expected Results: Documents containing Apply or Apple

Example Search Term: F?x

Expected Results: Documents containing Fix, Fox, Fax, etc.

Multi Character Wildcard: *

Example Search Term: Appl*

Expected Results: Documents containing Apply, Apples, Application, Appliance, etc.

Example Search Term: Fa*

Expected Results: Documents containing Fax, Faxes, Fade, Fad, Fail, etc.

Fuzzy Searches

Fuzzy searching will find a word even if it is slightly misspelled. For example, a fuzzy search for "liti3ation" will find "litigation." Fuzzy searching can be useful when searching text that may contain typographical errors or for text that has been scanned using optical character recognition (OCR). More specifically, Fuzzy searching finds all terms with a maximum of one change, where a change is the insertion, deletion or substitution of a single character, or transposition of two adjacent characters.

Fuzzy Search Character: ~

Example Search Term: Brwn~

Expected Results: Documents containing Brown, Brawn, etc.

Example Search Term: Erica~

Expected Results: Documents containing Erika, Ericka, etc.

Example Search Term: Hide~

Expected Results: Documents containing Hides, Hider, Hid, etc.

Proximity Searches

A proximity search allows users to specify a maximum distance between words and/or phrases in a document. Proximity searches in LEP search for the terms in either order, so in the example "fox quick"~5, even though the word fox appears first, documents where quick appears before fox will still be returned.

The closer the text is to the order specified in the query string, the more relevant that document is considered to be. When compared to the above example, the phrase "quick fox" would be considered more relevant than "quick brown fox".

As demonstrated above, proximity searches must be enclosed in quotes, followed by the tilde, and then the desired distance represented numerically. LEP currently supports the following types of proximity searches:


Example Search Term: "quick fox"~5

Expected Results: Returns documents where the words quick and fox appear within five words of eachother.

Words with Wildcards

Example Search Term: "fox* quick"~10

Expected Results: Returns documents where the words fox, foxes, foxy, etc. appear within ten words of quick.

Words with Phrases

Example Search Term: ""quick brown" fox"~3

Expected Results: Returns documents where the phrase quick brown appears within three words of fox.

Words with Wildcards and Phrases

Example Search Term: "quick brown" fox*"~5

Expected Results: Returns documents where the phrase "quick brown" appears within five words of fox, foxes, foxy, etc.

**Nested and more complex proximity searches are not supported in OpenSearch at this time. Support for these types of searches is expected to be made available by the end of January 2022.

Metadata Searches

In addition to being able to filter on metadata fields, LEP also supports the searching of certain metadata fields via the Search page. The specific fields are as follows:

Metadata Fields Search Syntax


From metadata.from:


Bcc metadata.bcc:

Subject metadata.subject:


Doc Type metadata.docType:

Doc Title metadata.docTitle:

Doc Original Title metadata.docOriginalTitle:

Last Modified By metadata.lastModifiedBy:

Master Date metadata.masterDate:

All Metadata Fields metadata.\*:

Search Examples

Search Term: "congratulations promotion"~10 AND metadata.docType:Email

Results: Returns documents where the terms congratulations and promotion appear within ten words of eachother, and where the Doc Type is Email.

Search Term: metadata.from:"Kate Symes" AND

Results: Returns documents where the "From" field contains Kate Symes and the "To" field contains

Search Term: metadata.\*:("" OR "kate Symes")

Results: Returns documents where any of the metadata fields contain Kate Symes or

Searching a Date or Date Range

To search a date range, enter the range as shown below. Users must (i) use a hyphen as the separator, (ii) enter the date(s) in YYYY-MM-DD format, and (iii) capitalize the word "TO."

Specific Date

Example Search Term: metadata.masterDate:2019-01-25

Expected Results: Returns all documents with a Master Date of 1/25/2019.

Full Date Range

Example Search Term: metadata.masterDate:[2019-01-25 TO 2020-11-13]

Expected Results: Returns all documents with a Master Date of 1/25/2019, through and including, 11/13/2020.

Partial Date Range

Example Search Term: metadata.masterDate:[2019 TO 2020]

Expected Results: Returns all documents with a Master Date of 1/1/2019, through and including, 1/1/2020.

Search Quick Links (Shared Function)

A list of all searches are automatically saved in the database whenever the user enters a keyword and clicks on Search. Search records are preserved on the list view under the Recent and creates filter hyperlinks to open a specific set of documents. The default link titles are under the Recent Search YYYY-DD-MM format. To rename, click the Edit hyperlink. The user may also pin and share the searches, delete existing searches or Cancel.

Search Quick Links are divided into three subsections:

Shared. To share or unshare searches with other users in the current case, click on the Edit link. When the Shared/Pinned Searches dialog box appears, check or uncheck the shared icon checkbox. This option will share searches with all current users that have access to the case.

Pinned. Click on the Edit hyperlink, select a recent search by title and use the checkbox under the pin symbol to pin searches. Click OK to save the changes or Cancel. The pin icon will move the selected search to the Pinned section.

Recent. Shows the search history applied. The screen displays the five most recent searches. Click the Edit hyperlink for a full list of searches.

Sorting Search Results by Built-in and Custom Fields

When searching for specific documents, optimize the search engine performance and sort the search results by Built-in Doc and/or Custom Doc Fields from the Filters option to the left of the search results window. Sorting of any field or combination of fields is also available by exporting the desired fields as columns into Excel and then sorting using Excel sorting functions.

Document Viewer -- Hits Tab

The Search is designed to open documents in the Hits view. Users can easily see where in the document the search terms are being found. Click on the Text hyperlink next to each section of hits to jump to that place in the document.

Document Viewer -- Document Comparison Tool

The built in document comparison tool can be used directly from the Search page. For more information see Document Comparison Tool.

Layouts and Layout Quick Links (Shared Function)

The Search page also allows users to save preferred field layouts and views. See Shared Features (Browse & Search) for more information.

ESI Culling

The process of culling (reducing or filtering) electronically stored information (ESI) is designed to defensibly reduce a large collection of documents to a smaller set of potentially responsive documents requiring further review. Depending on how documents were collected and other factors, culling can reduce data sets by 90% or more (if broadly collected) and thereby significantly reduce eDiscovery costs. Culling can be done by Custodian, by date range, and by text. See ESI Culling for more information.

Lexbe Professional Services is available to assist with any culling needs or keyword searching. Contact your sales consultant for a quote, or email to speak to a Professional Services representative about any questions related to searching or culling.