These are inspiring examples of computational advances that are beginning to define the next generation of metabolomics analysis capabilities, which could not have been developed without the availability of comprehensive and high-quality open spectral libraries. Although these are already exciting advances in their own right, we believe that this is only the beginning of a more data-driven approach to computational metabolomics. Especially with the emergence and commodification of deep learning approaches, the availability of large training data is paramount to achieve optimal performance. Deep learning is an extremely powerful class of machine learning models that especially excels in deriving complex patterns from massive amounts of data and discovering otherwise hidden data structures . However, care must be taken-as with any learning approach-that the analyses are reproducible and findings are carefully validated using follow-up studies . In other words, computational tools, including those based on statistics or machine learning, can help investigators formulate hypotheses, but it is critical that any discoveries made with such tools are confirmed using follow-up experiments designed to refute the hypotheses. However, as public spectral libraries continue to grow, we excitedly anticipate that this will further power the development of creative machine learning and other computational solutions to provide further tools in the researcher's arsenal to understand the rich data content that untargeted metabolomics provides.