This approach to spectrum-to-structure conversion is efficient if detailed prior knowledge on the detected feature is available, especially if the candidate structures are well defined and limited in number. However, such information is often not available or not sufficiently detailed. Ideally, one would use a large IRIS spectral library, similar to those available for MS/MS spectra, to facilitate quick and high-throughput screening. Large IR spectral libraries are available for neutral (gaseous) molecules, but their ions formed in MS (e.g. [M+H] + , [M-H] -, [M+Na] + ) possess drastically different IR spectra . An experimental IRIS library sufficiently large for identification purposes is currently unavailable (although efforts are underway to compile small libraries for specific compound classes, such as glycans ). Moreover, in the light of the vastness of small-molecule chemical space , experimental reference libraries are intrinsically limited for metabolomic studies, as is also the case for MS/MS and IMS data. Therefore, the focus in the metabolomics community has shifted towards expanding MS/MS and IMS libraries with computational data . In addition to their costeffectiveness, in silico libraries have the advantage of a greatly increased coverage, since only a small fraction of molecules are available as physical reference standards.