An important aspect of uncertainty quantification (UQ) methods is how to evaluate the performance of the uncertainty predictions made by a method. Here, it is relevant to consider some of the typical applications of the uncertainty estimates. Machine learning models are often created to be employed in high-throughput screening studies where the goal is to end up with a few candidate molecules with high probability of being good at whatever they where optimized towards. For this application, focus will be on the low uncertainties being adequately described with a significantly lower expected error, increasing the probability that predictions for the final candidates are correct. Another important application is sequential learning strategies such as Bayesian optimization and active learning, where the uncertainty estimates in conjunction with the predicted property are used to guide the choice of the next pool of training molecules. Since the uncertainty estimate associated with a molecule is directly linked to its probability of being picked, here the priority would be a decent performance across the range of uncertainties, especially the large ones.