The datasets presented here are larger than any previously reported set of experimental or computational IR ion spectra and provide further opportunities for improvement of the metabolite identification workflow. For instance, adapting the scoring method to better capture structural similarity , applying chemical element-dependent frequency scaling to better correct for anharmonic shifts , integrating IRIS scoring with MS/MS scoring algorithms , or tackling the spectrum-tostructure conversion directly 66 may be realized with this data set. The experimental and computation spectra are now available through the HMDB website and will be added to the Spectra Search interface in its next release (HMDB 6.0). Moreover, we will continue to expand both the experimental and in silico IR spectral library. A promising prospect in this regard is the development of machine-learned density functionals, which should significantly speed-up DFT calculations . This could extend the feasibility of our approach to molecules of increased size and with a much larger coverage of chemical space, thereby further establishing infrared ion spectroscopy as an appealing route for small-molecule identification far beyond metabolomics alone.