We use the Random Forest (RF) regression model implemented in scikit-learn, with 200 trees and a minimum of three samples per leaf node. The predicted logP value is the average over all trees and the uncertainty in the prediction is the corresponding standard deviation. The molecules for the training and test set are taken from a 250k molecules subset of the ZINC data base, which has been used in many other studies. A training set of size N corresponds to the first N molecules in the data set and the 5k test set corresponds to the last 5k molecules in the data set. For the molecular representation we use Morgan extended connectivity fingerprints with a diameter of four (ECFP4) as implemented in RDKit. This method identifies fragments of varying sizes centered at each atom of the molecule, with the maximum size determined by the radius. Each fragment is then assigned a random position in a binary vector of length 2048, where the presence and absence of a particular fragment is indicated by a 1 and 0, respectively. Other bit-vector sizes are also possible, but 2048 is a very typical value. The number of different fragments for a collection of molecules is typically much larger than 2048, meaning that a bit position can report on many different fragments, which is known as bit collision.