Machine learning models also exhibit biases on the exact data they were trained on. It is often the case that models have near perfect predictive performance on the training data, while performance on hold-out data is usually lower. This is achieved by making predictions based on spurious patterns that can be used to link samples to their labels, but are not true explanatory factors of the output. Consequently, when the outputs of a learned model are optimized these spurious patterns might be recovered. Additionally it is often observed that actives/inactives from the training set receive higher/lower prediction scores than those in the test set. This might bias the generation towards compounds similar to the training set actives as scores are skewed in favour of those. We refer to these problems as data specific biases.