Model details are collected in the supporting information, but here we present a short overview. We use a series of ML models to predict Crippen logP from a recent study combined with different UQ methods for obtaining uncertainty estimates. Here, we test two kinds of models: random forest (RF) trained on ECFP4 fingerprints and graph convolutional neural networks (GCNNs). As RF models are ensemble models, there is an intrinsic uncertainty estimate given by the standard deviation (σ) of the tree predictions. Janet et al. suggested using latent space (LS) distances to quantify uncertainty when working with deep learning models , which we use as uncertainty estimates for the logP GCNN models.