Regression models for free energy prediction were ensemble models 64 of multi-layer perceptrons with ReLU 65 activation functions. Each individual perceptron was trained on 5-fold split of the training data, each leaving out one fold for cross-validation, and was initialised with different weights and biases. Each produced different predictions for ligands in regions of chemical space where insufficient training data was available. Averaging over the predictions of independent models allows us to recover not only more precise values, but also more accurate ones . Final predictions in most of this work came from means of 5 models with standard errors used for uncertainties. However, iterations four and five of active learning on the prospective library further expand the ensembles to average the final prediction over five repeats of the above cross-validation training procedure, leading to averaging over 25 individual models in total for these iterations.