As an alternative to traditional VS, deep generative methods have transitioned from research protypes to practical and powerful tools for computational drug design . Such models are responsible for the design of multiple experimentally validated hits, including potent small molecule inhibitors for a variety of targets and PROTACs . Rather than screening a fixed, finite library, generative chemical models propose novel molecules based on probabilistic principles, allowing them to address very large chemical design spaces . These de novo design models consist of a generative component responsible for sampling molecules and a mechanism for steering the design to molecules with target properties.