In conclusion, we want to re-emphasize the exciting times ahead for spectral libraries in metabolomics. The community has become increasingly aware that capturing metabolomics knowledge in the form of reference MS/MS spectra accelerates discoveries. Existing spectral libraries have grown tremendously in the past few years, and we expect this growth to continue. Bigger libraries, especially those that are freely available for community use, will enable researchers to get more and better annotations from their data and achieve important biological insights. Additionally, it will be possible to develop increasingly powerful machine learning algorithms by training them on large spectral libraries. As some of these machine learning tools will improve the annotation rate in metabolomics and derive more value from existing and new data, this will make it possible to annotate new high-quality MS/MS spectra for inclusion in the next iteration of spectral libraries. As such, the growth of open spectral libraries and development of machine learning tools will proceed in lockstep to power a virtuous cycle and advance metabolomics in the upcoming years.