For single-digit numbers of drug-like ligands, this is still trivial in terms of cost. For large sets of ligands, however, it becomes advantageous to turn to ML models, which also appear to have more favorable error profiles -SVM models exhibit a much narrower error distribution across the entire molecular mass range than TI-based estimates. Crucially, the key advantage of ML HFE models is that they exhibit no real dependence between accuracy and ligand molecular weight -the ΔG error distribution for the ML predictors does not broaden moving to the right, unlike the distribution for TI-based estimates. In principle, the downside to ML models is that they are much better at interpolating than extrapolating, meaning that they can be used only when data is already at hand for compounds similar to the ones of interest. ML models such as the ones we present here are much less useful when presented with novel chemical series, which is where all-atom HFE calculations, being much closer to first principles, shine. Nevertheless, our SVR predictors outperformed our TI estimators, despite the fact that FreeSolv is not composed on a single series of compounds. The other caveat concerning our ML results is that they are based on limited training and testing data where the number of descriptors is comparable to the number of train and test data points. Robust conclusions can only be drawn when the number of test data points is more than 10 times the number of descriptors.