Besides their primary function for spectrum annotation, spectral libraries are also an extremely valuable resource to develop machine learning approaches for the analysis of mass spectrometry data . In proteomics, the availability of high-quality spectral libraries that can be used as large-scale training data has spurred the development of several innovative deep learning tools. For example, Prosit is a deep neural network that was trained on the ProteomeTools library to learn peptide fragmentation patterns and predict MS/MS fragment intensities with high fidelity . MS/MS spectra predicted by Prosit, as well as related tools that were developed in a similar fashion , are now regularly used in lieu of experimental spectral libraries, for example, for the analysis of DIA data without the need to acquire a custom spectral library in advance. This illustrates how the important effort of synthesizing and measuring peptide standards provides continuing benefits outside of the original study by enabling the development of deep learning methods that can be used to simulate highly accurate MS/MS spectra for novel peptides to complement experimental spectral libraries. Similarly, MassIVE-KB was recently used to develop the GLEAMS neural network that can efficiently process hundreds of millions of MS/MS spectra at the repository scale to explore the dark proteome .