We discussed possible failure modes in goal-directed learning, with an emphasis on problems that are introduced when using machine learning models as scoring functions. While many technological tools and techniques are suitable to optimize a scoring function, the central challenge lies in the scoring function itself. We showed that optimization exploits model-based scoring functions using their data-and model-specific biases. Given that the original aim of generative models for de novo design is to explore all of chemical space, biases towards the training data may indicate failure in this respect. We believe that control scores can help in detecting such biases and guide practitioners to potentially more meaningful results. Future experiments should investigate more closely the influence that the choice of classifiers and molecular optimization methods have on the gap between optimization and control scores.