With the proliferation of search engines for the analysis of MS data, multisearch techniques aimed at boosting the discriminating power of the search engines' score functions have recently become popular. Much statistical and algorithmic work has been done, therefore, in order to be able to combine and parse multiple search streams. However, multisearch techniques suffer from long run times, and may have little impact on false negatives because of similar peptide filtering heuristics between searches. This review focuses, rather, on multipass techniques, which use the results of one search to guide the selection of spectra, parameters and sequences in subsequent searches. This reduces the number of false-negative peptide identifications due to peptide candidate filtering while preserving statistical significance of existing (correct) identifications. Furthermore, this technique avoids substantial increases in running time and, by limiting the search space, does not reduce the statistical significance of correct identifications or introduce a statistically significant number of false-positive identifications. However, we argue that the existing combiner tools are not reliably applicable to these multipass situations, because of algorithmic assumptions about search space and statistical assumptions about the rate of true positives. Here we provide an overview of the advantages of and issues in multipass analysis techniques, the existing methods and workflows available to proteomic researchers, and the unsolved statistical and algorithmic issues amenable to future research.