NP-Scout, reported in the year 2019, is a practical machine learning (ML) approach based on random forest classifiers for the differentiation of NPs and SMs and for the quantification of NP-likeness. An additional value of NP-scout is the implementation of similarity maps to visualize atoms in molecules making decisive contributions to the assignment of compounds to NP or synthetic molecule (SM). The similarity maps highlight the atoms that contribute significantly to the classification of small molecules as NPs or SMs.