False positive peptide identifications are a major concern in the field of peptidecentric, mass spectrometry-driven gel-free proteomics. They occur in regions where the score distributions of true positives and true negatives overlap. Removal of these false positive identifications necessarily involves a trade-off between sensitivity and specificity. Existing postprocessing tools typically rely on a fixed or semifixed set of assumptions in their attempts to optimize both the sensitivity and the specificity of peptide and protein identification using MS/MS spectra. Because of the expanding diversity in available proteomics technologies, however, these postprocessing tools often struggle to adapt to emerging technology-specific peculiarity. Here we present a novel tool named Peptizer that solves this adaptability issue by making use of pluggable assumptions. This research-oriented postprocessing tool also includes a graphical user interface to perform efficient manual validation of suspect identifications for optimal sensitivity recovery. Peptizer is open source software under the Apache2 license and is written in Java.