The neural networks employed in those works have a powerful learning capacity but display a limited model explainability for identifying the features (in our case, the molecular properties) that affect the target property (here, the ℎ .# value). In most cases, the investigations employ such a large number of descriptors that it becomes almost impossible to identify the real molecular origin of the impact sensitivity. Moreover, while some descriptors have a direct chemical interpretation (e.g., an aromaticity index), others such as WHIM (Weighted Holistic Invariant Molecular), which is based on a principal component analysis of the weighted covariance matrix from atomic cartesian coordinates, though helpful, are not straightforward to interpret. An additional issue concerning the number of descriptors is that the results produced by adding more of them may lead to overfitting and chance correlations. In an important work of ML applied to energetic materials, different machine learning models and several featurization methods were compared using a small training data set of 87 diverse 𝐶𝑁𝑂𝐻𝐹 energetic molecules, and a test data of 22 molecules, predicted different explosive properties with small errors, such as detonation pressure, detonation velocity, explosive energy, heat of formation, density, among others. This work is also significant for the present purposes because it showed that a small number of descriptors -in some cases, only one (e.g., oxygen balance) can provide good results for the target feature. Their main conclusion is that feature selection is more important than model selection for making property predictions on a small data set, a conclusion also confirmed by our results.