The NLL (both observed and simulated) decreases with increasing amount of training data. The trend in NLL sim is solely due to how the predicted uncertainty distribution changes since it does not depend on the observed errors. More accurate models means on average lower error and should be accompanied by smaller uncertainties which would result in lower NLL; this is the trend we observe in the NLL sim . For many of the models, the observed NLL is in decent agreement with NLL sim , i.e. we can not say that uncertainties and errors fit better for the model trained on 150,000 molecules compared to the model trained on 1,000 since both have NLL ≈ 2 standard deviations from NLL sim . For some (especially the models trained on 100 and 500 training samples), there is a big discrepancy between NLL and NLL sim which points to a problematic behaviour of the predicted uncertainties. In particular, the observed NLL is much higher than NLL sim indicating that the errors are generally higher than expected so the uncertainties are underestimated; we see this behavior clearly in the error-based calibration plots for N train = 100 and N train = 500 in Figure .