Mass spectrometry is one of the essential techniques for the identification and analysis of small molecules. For example, during the development of new molecules, unidentified molecules are sometimes produced by side reaction and need to be identified. Mass spectrometry coupled gas chromatography (GC-MS) is commonly used for the identification. GC-MS was standardized more than 50 years ago with electron ionization at 70 eV and, its spectra data have been accumulated in the database , However, the chemical space of the database includes less than millions and is considered small because more than 100 million compounds are already known, and many unknown spectra are detected . To overcome the current situation, i.e., the limited coverage of the experimental database, extending the database by computationally estimating spectra from molecular structures is used. For the in silico fragmentation, quantum chemical and machine learning methods have been proposed. Quantum chemical electron ionization mass spectra (QCEIMS) is an automated method that combines ab initio molecular dynamics with statistical treatment. It can predict mass spectra without a database or empirical fragmentation rules but needs a high computation cost . On the other hand, neural electron-ionization mass spectrometry (NEIMS) , a deep neural network for EI-MS prediction, can generate mass spectra for 1 million molecules in 90 minutes. In addition, NEIMS is better than existing machine learning models by linking encoded vector by residual networks (ResNets) to a building block called bidirectional prediction, which incorporates some of the physical phenomena that occur in mass spectrometry . In this study, we numerically assessed NEIMS for highly fluorinated compounds. Based on the numerical assessment, we propose an undersampling algorithm using the molecular similarity-wise sampling (MSS) algorithm. The proposed method was based on the similarity estimated by extended-connectivity fingerprints (ECFPs) . Since this approach is independent of the details of the mass spectrum, it can be applied to other machine learning models using molecular big data.