Perhaps the greatest hurdle for the widespread molecular characterization of the metabolomes of non-Saccharomyces yeasts is a lack of accessible, well-curated metabolite databases. Identification of metabolites using untargeted MS approaches typically relies on matching experimental data with known database entries based on molecular mass, characteristic MS/MS fragment ion abundances and associated data such as retention time (Fig. ). While several high-quality curated MS databases are available, including NIST, METLIN, mzCloud, MassBank, and HMDB, these databases only contain MS/MS ion fragment spectra for a small fraction of the more than 100 million compounds listed in databases such as PubChem . Substantial bias also exists in the types of compounds present in databases, with poor coverage of rare or low abundance metabolites. There is also a bias towards spectra acquired by GC-MS, because of its earlier development. This is particularly problematic because LC-MS has emerged as a popular preferred technique for metabolomics, with several different MS instrument designs and fragmentation techniques. It is therefore vital that the community continues submission and curation of well-annotated data generated with a variety of instruments and methods, including the submission of newly discovered compounds. Unfortunately, community generated datasets have their own potential problems, chief among which for metabolomics data are inconsistent naming conventions. To combat these issues, the Metabolomics Standards Initiative recommended in 2007 that a central repository for metabolite data with strict submission guidelines be created .