The three methodologies employed for the calculation of the NP-likeness scores have a good performance distinguishing NPs or identifying NP-likeness, but have different definitions of NP likeness. The NPLC score, which reimplements the well-known Ertl score, is based on fragments that are typical for NPs with the goal to create a score that evaluates the NP likeness. In contrast, the N3PL network was trained to identify natural products and thus it evaluates how likely a molecule is a natural product. Menke et al. describe a good example of a furan which is ranked high by the network score but low by Ertl's score. It is part of the COCONUT database but have many specific NP-like features. In addition, the neural network based N3PL score does not only take structural features into account, but also surface descriptors. This means that this definition is not only a structural NP-like score but also a NP-property like score. This leads to slightly different outcomes in the NP-likeness analysis. From the pure structural point of view, the NPLC score revealed a large variability between the analyzed NP collections and distinct combinations of NP fragments present in LANaPDB. The NP-likeness profile determined with NP-Scout and N3PL indicates that the NPs of LANaPDB have a probability of 100% or near to 100%, to be labeled as NPs. According to the results of NP-likeness calculator and NP-Scout, the FDA-approved small-molecule drugs reference dataset is composed of more synthetic-like molecules. The N3PL shows a distribution that is slightly shifted to more NP likeness. This is due to the fact that desired drug properties are of course similar to NP properties.