GBT is another ensemble method relying on decision trees. In this case, the decision tree classifiers are fitted in stages for the whole training dataset, where each subsequent tree is intended to "correct" the errors made by the previous one in terms of a loss function, usually the deviance of the fitted model with respect to a perfect model. For this algorithm, the number of decision trees was fixed to 1000, the number of features to consider when looking for the best splits in the individual trees was optimized in a representation-dependent manner using candidate values of 1, 2, 3, 4 and 5 times the square root of the number of features in the fingerprint representation, for the maximum depth of the individual trees we used candidate values of 4, 6, 8 and 10, and for the minimum number of samples to split an internal node in the individual trees we used candidate values of 2, 3, 4, and 5.