RMSE 0.28 0.16 The change in model from RF/ECFP4 to GCNN results in a significantly lower test set error (Table ). For the uncertainties, we test two versions of the latent space (LS). The GCNN consists of a graph convolutional network (GCN) ending in a pooling layer resulting in a vector representation of the molecule which is followed by a fully connected neural network (NN) ending in a prediction. LS-NN represents uncertainties based on the last layer of the NN and LS-GCN represents uncertainties based on the learned molecular feature vector. The latent space uncertainties are derived from the average distance in latent space of a test data point to the k nearest neighbors in the training set. We use k = 10 throughout this work. : UQ evaluation metrics for the latent space uncertainties of a GCNN model trained on 9k+1k data points of Crippen's logP. For NN uncertainties, the latent space used is the very last layer of the NN. For GCN uncertainties, the latent space is the vector right after the pooling layer (LS-GCN). The UQ evaluation metrics for the LS-NN uncertainties and LS-GCN uncertainties including linear fit parameters for the error-based calibration are summarized in Table . From the error-based calibration plots (Figure for the LS-NN uncertainties and Figure for th LS-GCN uncertainties) we notice the very poor quality of the linear fit for the 9k+1k model using either LS-NN uncertainties (R 2 = 0.31) or LS-GCN uncertainties (R 2 = 0.24). Thus, while e.g. LS-NN uncertainties with a 10k training set show close to optimal slope (1.12) and intercept (-0.05), from the error-based calibration plots, we see that the 10k uncertainty estimates by no means follow the expected diagonal line (Figures and). For the uncertainty bins with RMV < 0.34 the RMSE-RMV correlation seems quite random; for the LS-NN uncertainties there even seems to be a negative correlation for the lower half of the uncertainties. These observations are in agreement with the close to zero Spearman's rank correlation coefficients observed for these uncertainty estimates (Table ) suggesting no correlation between error and predicted uncertainty. This phenomenon of zero ρ rank is not uncommon; Hirschfeld et al. observed ρ rank values close to zero or negative for a significant part of the tested UQ methods . The simulated ρ sim rank is also quite low for the 9k+1k model using either LS-NN (0.07±0.01) or LS-GCN (0.11±0.01) uncertainties, though still significantly higher than the observed coefficient pointing to a rather narrow distribution of predicted uncertainties but also some problem with the uncertainties, which is obvious from the error-calibration plots. Thus, based on Spearman's rank correlation coefficient we would deem these uncertainty estimates completely useless. While it is obvious that we should not use these uncertainty estimates as a way of pointing to samples with increased probability of low error, we see that the very highest uncertainties can be used as a predictor for a higher probability of high error; a detail lost by looking at a single-valued metric such as a correlation coefficient. The performance of the uncertainty estimates changes completely for the model with 145k+5k training samples. The LS-NN 150k uncertainties show a much better linear correlation (R 2 = 0.91) and the RMSE bins are distributed around the expected diagonal (Figure ).