For experiment 11, we trained a Gaussian Process with Radial Basis Function kernel and utilized the Information Gain to select molecules to add to the training set. It has been shown that iteratively trying to maximize the information gain of a Gaussian Process is equivalent to selecting the unlabeled data point with the largest variance under the posterior predictions. Thus we can perform the selection according to Equation .