Larger chemical spaces can also lead to unintended consequences: Since most scoring functions, in particular those for predicting biological endpoints, are imperfect proxies, with more fine-grained sampling of the chemical space it becomes more likely that limitations of the scoring function are exploited to give false-positive molecules whose predicted properties to not match wet-lab experiments . This phenomenon is also known as Goodhart's law, often stated as "When a measure becomes a target, it ceases to be a good measure". Alternatively, this can be described as the selection of molecules outside of the applicability domain of the scoring function, or, from the ML perspective, as an unintended adversarial attack on the scoring function, or in the context of reinforcement learning as reward-hacking.