Machine learning (ML) plays a crucial role in the field of drug discovery, allowing the rapid prediction of molecular properties and biological activities. By leveraging vast data sets on chemical compounds, ML models have demonstrated remarkable success in identifying potential drug candidates, optimizing lead compounds, and predicting pharmacokinetic properties. This computational acceleration of early-stage drug development has substantially contributed to the reduced need for extensive experimental testing. Yet, a critical challenge persists in the field: while ML models typically work well on in-distribution (ID) data, i.e., molecules similar to those in their training sets, they are often challenged by out-ofdistribution (OOD) data, i.e., molecules that differ substantially from those in their training sets.