As an example of spectral library curation, a strategy for inter-library comparison was described to detect mis-annotated outliers by visual inspection based on an extensive checklist of potential issues . Manual quality annotations have limited scalability, however, as they depend on scarce expert user knowledge and require a significant time investment. Because such domain experts often produce very trustworthy manual spectrum annotations and their expertise is not (yet) translated into community knowledge, this represents a unique opportunity to further improve the quality of spectral libraries. Alternatively, some computational approaches for MS/MS spectral library assessment have been proposed, including spectral entropy. Entropy is often likened to the disorder of a system. For example, there are more disorderly states in which a deck of cards can occur in random order (high entropy) than those in which the deck occurs in sorted order (low entropy). Spectral entropy was recently proposed as a measure to assess the quality of MS/MS spectra, with lower-quality spectra receiving higher spectral entropies. For example, there are small differences in the spectral entropy distributions of the highly curated NIST spectral library and more heterogeneous spectral libraries from MoNA and GNPS (Figure ). Nevertheless, we would argue against a simple maximum spectral entropy cut-off to determine whether MS/MS spectra are of sufficient quality. There is a strong (nonlinear) relationship between spectral entropy and the number of fragment ions, with MS/MS spectra that contain only a few fragment ions getting low spectral entropy scores . Although such spectra might be arguably of higher quality and more "clean," this could also indicate that some of the spectra with low spectral entropy contain insufficiently discriminative fragmentation information to achieve sensitive MS/MS annotation. Nevertheless, spectral entropy is an interesting criterion to support the automated quality assessment of MS/MS spectra, which forms an open challenge that warrants additional research. Besides the quality of the library spectra, the veracity of the matches between library spectra and experimental spectra is also essential in determining whether to accept spectrum annotations. Typically, valid spectrum annotations are accepted based on common heuristics, such as a minimum cosine similarity threshold of 0.7 and minimum 6 matching peaks (M. . However, such heuristics do not provide a statistical confidence estimate of the spectrum annotations, and as such, the number of false positives (i.e. incorrectly accepted high-scoring annotations) and false negatives (i.e. missed low-scoring annotations) are unknown. Although not widely used yet at this time, there are emerging strategies for estimating the false discovery rate of MS/MS spectrum annotations. For example, the Passatuto approach constructs a decoy library by modifying MS/MS library spectra based on re-rooted fragmentation trees to enable estimating false discovery rates using a target-decoy strategy . This allows the researcher to accept spectrum annotations with a controlled false discovery rate such that they can decide how many incorrect matches they are willing to include in their results. Although a few other methods to control false discovery rates in metabolomics have been introduced , none are currently routinely used. Statistical control of MS/MS spectrum annotations is an important area of research to explore further and advance untargeted metabolomics into a highly scalable quantitative technique, and we anticipate that such tools will become routinely accessible in emerging MS/MS-based spectrum annotation software.