There are still some challenges associated with generating and using spectral libraries in metabolomics, however. Many spectral libraries have a considerable amount of missing information. When compiling crowd-sourced spectral libraries, there is a trade-off between requiring that all metadata has been unambiguously specified, which entails an additional time commitment and complexity for users submitting their data, and freely accepting contributions. The former results in a higher barrier towards contributing data to community spectral libraries, leading to smaller spectral libraries, while the latter results in less defined spectral libraries. As it is very challenging to completely eliminate all mistakes from spectral libraries, it is of the utmost importance to understand the provenance of spectral library matches. This allows the end user to make informed judgment calls to decide whether the matches should be followed up in subsequent experiments. Popular community spectral libraries, such as GNPS and MoNA, address this dichotomy by using a multi-faceted ranking system to rate individual MS/MS spectra, contributing users, and MS/MS assignments. Furthermore, a critical evaluation of any results by the user, irrespective of the spectral library source, is essential.