Twenty years ago it was standard practice to apply somewhat arbitrary sequence search engine score thresholds (e.g., SEQUEST xcorr > 1.5 for 1+ peptidespectrum matches (PSMs), xcorr > 2.0 for 2+ PSMs, xcorr > 2.5 for 3+ PSMs, etc.) to search results, perhaps manually looking at some spectra near the thresholds to keep some and discard some, with minimal grasp of the error rates present in the final results. Diligent researchers would spend a few days or weeks looking at spectra, subjectively discarding some and keeping others given the comparatively small size of the data obtained. This dramatically changed in 2003 with the advent of PeptideProphet and ProteinProphet , which applied Bayesian machine learning techniques to the various search engine scores to model the correct and incorrect assignment distributions and then use these models to assign a probability of being correct based on these learned models. With these tools it became possible to validate search engine results on large-scale datasets in a short order, enabling users to select probability thresholds based on a selected tolerable false discovery rate (FDR). A few years later, these two software tools plus some quantitative tools were bundled together and made fully interoperable via the open XML-based formats pepXML and protXML to become the Trans-Proteomic Pipeline 6 (TPP).