An important method used to speed up forensic file-system analysis is white-listing of files: Well-known files are detected using signatures (message digests) or similar methods, and omitted from further analysis initially, in order to better focus the initial analysis on files likely to be more important. Typical examples of such well-known files include files used by operating systems, popular applications, and software libraries. This paper presents methods for improving the effectiveness and efficiency of such signature-based white-listing during file-system forensics. One concern for effectiveness is the resilience of the white-listing method to an adversary who has complete knowledge of the method and who may make small, inconsequential changes to a large number of well-known files on a target file-system in order to overload the analysis and thereby practically defeat it. Another concern is the ability to detect near-matches in addition to exact matches. Efficiency refers to primarily the rate at which a target file system may be processed during analysis; preparation-time, or indexing, efficiency is a lesser concern as that computation may be performed during non-critical times. Our work builds on techniques such as locality-sensitive hashing to yield an effective filter for further analysis tools.