The first approach is gradient boosting machine (GBM), which is a random forest based technique, utilising ensembles of decision trees. These trees are created from random subsets of features (fingerprints), that are then used to make predictions. GBMs expand on traditional random forests by using the gradient of the error to construct trees specifically designed to minimise this error. Gradually increasing the number of relatively poor individual trees additively increases their predictive power (hence 'gradient boosted'). The second model is Gaussian Process (GP) regression, which is a Bayesian approach that makes predictions by assuming observations can be modelled by the probability distribution over possible reasonable (Gaussian) functions. These Gaussian distributions are iteratively refined by the observation of new samples. Because model prediction is performed via a probability distribution, it natively incorporates uncertainty and other useful quantities, such as estimates of expected improvement of a given new sample. The acquisition function defines the method by which new molecules are picked at the start of each active learning cycle, with the simplest example being a 'greedy' approach, which directly selects the (currently predicted) highest scoring molecules. However, an acquisition function has to balance picking the best compounds, with the need to further refine the accuracy of the machine learning model. Picking the best scoring candidates in descending order might initially increase the objective function, but the algorithm will have the propensity to get stuck in local maxima and to be sensitive to the initial selection of training molecules.