Despite their impressive growth in the past few years , spectral libraries still only cover a minor part of the known chemical space. For example, PubChem currently contains information for 112 million unique compounds (September 2022), whereas all metabolomics spectral libraries combined account for less than 1% of those molecules. As spectral library searching can only annotate known molecules with reference MS/MS spectra or related molecules using analog searching, "unknown unknowns," where experimental MS/MS spectra did not match any of the reference spectra included in the spectral library, cannot be identified . Some spectral library providers have started to integrate in silico MS/MS spectra alongside experimental MS/MS spectra to partially address this issue. Especially as spectrum prediction tools are getting increasingly better, this could be a viable strategy to expand the coverage of spectral libraries. At present, however, we strongly urge caution when accepting annotations based on simulated spectra only. It is still easiest to assess whether an MS/MS spectrum match is acceptable based on the user's search criteria through manual inspection of experimental MS/MS data. Rather than being able to simulate MS/MS spectra for all 112 million compounds in PubChem, we anticipate that in silico spectra could be a valuable addition for a subset of specific molecular families for which the performance and quality of spectrum prediction tools is well understood and has been carefully validated. For example, high-fidelity peptide mass spectra simulated by deep learning-powered spectrum prediction tools are being increasingly incorporated into various proteomics bioinformatics workflows , and the LipidBlast library, which consists of approximately half a million simulated MS/MS spectra, is available through MoNA to annotate lipids . Furthermore, there is a mismatch between the compounds included in spectral libraries and the MS/MS spectra observed in experimental data. For example, out of 586,647 MS/MS spectra present in the GNPS community spectral libraries, 22% have been found in experimental datasets deposited to GNPS (Figure ). This indicates that many of the compounds represented in reference libraries are not observed in metabolomics experiments, or that the library MS/MS spectra were created in a different fashion than for experimental data, such as when the preferred ion form is not included (Figure ). Notably, even as the public libraries have grown spectacularly over the previous decade, the rate of matched library spectra has remained relatively consistent. This illustrates the previously described bias in the commercial availability of pure reference compounds that are typically used for spectral library creation efforts. It also indicates that many relevant biological compounds are currently still missing from available spectral libraries, and that careful prioritization of the reference compounds to include is an important aspect of generating spectral libraries that provide maximum benefit.