The overall performance can be captured in a single number by taking the geometric mean of the rank of each of the 189 reference metabolites . This rank product (RP) is equal to 1.3 here. When the IR spectra from different adducts ([M+H] + , [M-H] -, [M+Na] + ) of the same metabolite are combined (blue solid trace) by ranking on the product of individual spectral similarity scores, the correct identification rate increases to 83% (72 out of 87 metabolites at rank #1) and the RP improves to 1.2. Overall, this performance is higher than typical retrieval rates when employing tandem MS libraries, which typically are 45-70% , although true comparison is hampered by the much smaller validation set used here. What the present results do clearly demonstrate is the general applicability of the in silico IR spectral library in identifying compounds across a large data set. A similar plot can be constructed to assess how well metabolites are retrieved in an unsupervised search, i.e., releasing the m/z constraint (Figure ) and matching the IRIS spectrum against all adductconstrained computed spectra in the IR library. In this case, 19% of the identifications are correct, while 48% are scored in the top 10. The specificity improves significantly when IR spectra of different adducts ([M+H] + , [M-H] -, [M+Na] + ) are combined, boosting the top 1 and top 10 percentages to 32% and 62%, respectively. The RPs follow a similar trend, improving from 18.2 to 7.6. For the unsupervised search, the contrast with randomly annotating the experimental spectra is pronounced, with just 0.03% correct identification, as the number of adduct-constrained candidates increases to 3892 on average.