Machine learning algorithms have been widely used in virtual screening approaches. Supervised learning techniques use a training and test datasets composed of known active and known inactive compounds. Different ML algorithms have been applied with success in virtual screening strategies, such as recursive partitioning, support vector machines, k-nearest neighbors and neural networks. These models find the probability that a compound is active and then ranking each compound based on its probability.