ML models generally show significant performance degradation when tested on OOD data and often fail when tested on examples outside the training domain. These limitations are particularly relevant in drug discovery, where exploring novel chemical spaces beyond training data boundaries is essential for identifying new therapeutic compounds. Although such exploration is crucial for innovation, it exposes ML models to OOD scenarios where their predictions may be unreliable. The ability to maintain accuracy and robustness when faced with novel molecular structures is, therefore, critical for applications of ML in drug discovery.