To evaluate the effect of the undersampling method using the molecular similarity, we used 236715 spectral data from the NIST17 main library. We undersampled the L i = 1 data by 70% with α in Equation (2) set to 0.1. A randomly undersampled dataset of the same size as the undersampled dataset was created for comparison. Before evaluating our method, we calculated ECFPs for datasets and examined the number of identifiers in which bit collision occurred, and found that there were approximately 2.5 × 10 5 bit collisions in the dataset used for training within the NIST main library. On the other hand, our proposed method reduced bit collisions by about 21%. The chemical spaces of pre and post sampling datasets were visualized by UMAP , a dimension reduction technique in Figure . The molecular structures were vectorized with mol2vec , where mol2vec is an unsupervised molecular representation learning method based on ECFPs. The ECFPs are used as NEIMS input data. The curved surface in Figure shows a probability density function estimated by the kernel density estimation method scaled by the size of the dataset. It is observed that many data have been excluded from the high density region. Compounds dissimilar to the target compounds are excluded, and bit collisions are mitigated, as shown in Figure .