The accuracy of the GNN, trained on ∼370'000 diverse small molecules with a molecular weight <500 Da, was assessed on an external test set consisting of 1000 compounds with a molecular weight ranging from 500 Da to 700 Da. The RMSE values achieved for each solvent are shown in Figure . The overall correlation between the GNN and the external test set is excellent (Figure ). The error varies significantly between different solvents ranging from 4.6 kJ mol -1 nm -1 for hexane to 46.9 kJ mol -1 nm -1 for HMPA. The significant differences between the solvents are most likely an effect of differing force magnitudes (see SI Figure for the reference force magnitude distributions). Apolar solvents generally feature smaller forces, while polar ones feature larger forces. In addition to these polarity effects, another aspect is likely the influence of solvent rigidity (i.e., the time it takes for a solvent to equilibrate around a given conformation) and could explain why HMPA, glycerin, and octanol show such large errors. While for most solvents, the sampling time to capture the Boltzmann-weighted ensemble of solvent configurations around a given conformer is large enough, these larger solvents may suffer from increased sampling errors in the test set calculations. As the GNN was trained on multiple conformers, it is possible that even though the test set accuracy is low, these errors do not manifest in large deviations of observables in prospective applications (e.g., free-energy profiles or conformational ensembles). For this reason, the sampling time for these solvents was not increased for the training set generation. Three models with different random seeds for the initialization were trained and evaluated. In general, the performance of the three models was virtually identical (i.e., standard deviation over the RMSE values is at most 0.05 kJ mol -1 nm -1 ). Hence, one model was selected for all further analysis. The model with the highest average RMSE for all solvents was chosen to be conservative.