As it would happen for a GA process, the different surrogates are employed to locally optimize the set and produce pools of respective LM. Therefore, the evaluation of a surrogate's performance must be based on its ability to not only approximate the energy, but also the coordinates of the reference LM; a satisfactory model should provide target structures with relative energies following the B3LYP/6-31G(d,p) ranking at best. Illustratively, the wells of the surrogate in Figure must be as "close" as possible to the reference wells, in terms of both energy and structure. However, as also depicted in Figure , we note that a direct (one-toone) comparison between surrogate and reference LM is not possible because similar initial points may relax into very different geometries depending on the method and optimizer used. Consequently, in the absence of side chains for GPGG, the backbone RMSD (bb-RMSD) was chosen as a metric to identify "closest" LM structures and rely on a more faith- We opted for a statistical analysis to mimic the various fitness evaluations during a GA run, which also informs about the performance of the surrogates for different population sizes: For a random subset of S initial structures taken from the test set, each reference B3LYP/6-31G(d,p) LM is associated with its closest (in terms of bb-RMSD) surrogate LM. Then, the relative energies within the subset are used to calculate the mean absolute error (MAE) of the surrogate energy: