To address the issue of reward hacking, strategies have been developed to assess and improve the reliability of prediction models. The reliability of these models has long been formulated as applicability domains (ADs) and has recently gained attention in the context of prediction uncertainty . The AD is defined as "the response and chemical structure space in which the model makes predictions with a given reliability" , and predictions within the AD are expected to maintain a certain level of accuracy. The trade-off between the size of an AD and the reliability of a prediction can be adjusted by changing the reliability level (Fig. ). A common approach to ensuring reliability is to guide the design of molecules within the AD of a single prediction model . Additionally, a strategy for performing multi-objective optimization by considering prediction reliability has been reported, where multiple ADs are merged into a single AD (Fig. ), and molecular designs are then performed within this merged AD .