The full refactoring of MS 2 Rescore resulted in a Python package that is easier to use for both end-users, and for developers and bioinformatics researchers. While the GUI and CLI can be used to start a complete rescoring workflow, separate steps of the pipeline can be accessed individually through the Python API (Figure .) This enables custom implementations of MS Rescore into new or existing data analysis workflows, but also facilitates experimentation with, for instance, different feature generators. The high level of modularity also drastically simplifies the addition of new feature generators or rescoring engines as soon as these become available. For example, we were able to quickly and effortlessly adopt the newly published ionmob predictor as feature generator, and now allow users to choose between Percolator or Mokapot as rescoring engine. The improved documentation and contributing guidelines allow anyone interested to contribute to the project by adding new functionality, new feature generators, or new rescoring engines. The performance of MS 2 Rescore 3.0 combined with the updated MS Amanda was evaluated using data from two MS workflows with different injection times of 500 ms and 750 ms, both from the single-cell proteomics data set published by Furtwängler and colleagues in 2022. Both data sets were searched with MS Amanda to obtain all potential peptide identifications, which were then post-processed with either Percolator or MS 2 Rescore. As depicted in Figure , the results clearly illustrate that the total number of PSMs identified at 1% FDR is higher when employing MS 2 Rescore in comparison to MS Amanda in combination with Percolator and the initial identifications from MS Amanda.