We first evaluated the performance of the undersampling method on 11,600 spectral data points from the NIST replicates library that were not used for validation during machine learning model training. The average of weighted cosine similarities is presented in Table . Compared to the model trained on the regular all data, the score of the model trained on the undersampled data decreased by 0.011. Although the data points were reduced for the highly fluorinated compounds, the decrease of the similarity is relatively small and does not affect practical applications for common compounds. We next examined the effect of our sampling algorithm to the target spectra predictions. The weighted cosine similarities between observed and predicted spectra and library search ranks are shown in Figure . By comparison between the original NEIMS and current models, the effect of undersampling is generally larger for compounds with lower predicted similarities in terms of similarities and ranks. The weighted cosine similarity increased for all compounds, with a maximum increase of 0.265. Regarding the library search rankings, the rankings are improved except for T-5, which was already ranked #1 in all data models. In particular, T-1, which was ranked 1005th in all data models, moved up 997 positions to 8th place. This is a practical rank that can be chosen as a candidate for molecular identification, suggesting that MSS improves the efficiency of molecular identification in the library match approach.