Our results demonstrate that, in general, all descriptors provide accurate and stable potentials. This is evident from the direct comparison of predicted and ground-true energies for two systems: a small cluster system comprising 27 water molecules, from which we extracted 200 configurations from a 1 ps MD simulation, and a larger system consisting of structured arrangements due to the presence of hydrogen bonds (HB). Finally, the upper cluster comprises configurations near equilibrium, evidenced by lower energies. Interestingly, the energy selector predominantly selects geometries near the equilibrium configurations (67%), with only a handful of configurations in the middle/bottom cluster. In contrast, the configurations selected by either the similarity or distance selectors are more evenly distributed (Fig. ). The size of the training set does not influence the MLP performance, as all MLPs reproduced well the experimental radial distribution function (RDF) of water (Fig. ). The small differences in the positions and amplitudes of peaks for the first and second solvation shells are likely due to the level of theory used and the lack of nuclear quantum effects. This demonstrates that both similarity and distance selectors explore the chemical space more efficiently, using only around 15% of the training data required by the energy selector.