While NEIMS performs well for the molecules and measures for which it was trained, accuracy declines for molecules (amino acids) outside of the training set and when accuracy is measured in other ways (SCA/RMSE). Despite training on over 300,000 molecules within the NIST mass spectral database. The pattern was consistent across all three accuracy metrics and all three libraries tested, albeit to varying degrees. This finding is perhaps intuitive given the small proportion of the NIST-MS amino acid spectra (~0.01%), but here we quantify to what degree this problem manifests. More surprisingly the data also show that neither derivatization nor physicochemistry (molecular weight and hydrophobicity), correlate with accuracy. While this might follow from the small fraction of amino acids within the NEIMS training set, it gives no clear insight into why NEIMS struggles to predict MS spectra for these amino acids reliably. In terms of derivatization, the enigma is that the NEIMS algorithm is just as accurate for 'free' amino acids as for their derivatized counterparts. Because MTBSTFA derivatization is designed to standardize the ionization and fragmentation pattern of amino acids: one could anticipate it would create a more consistent framework for predictive modeling. On the other hand, by replacing active hydrogens in hydroxyl, amino, and thiol groups with tert-butyldimethylsilyl (TBDMS), derivatization adds atoms and complexity which could produce the opposite effect. In fact we see neither: derivatized and free amino acids are indistinguishable in terms of prediction accuracy.