Neglecting the multiple modalities of HTS experiments has the major downside of discarding millions of lower-fidelity measurements that cover an orders-of-magnitude larger and more diverse chemical space. Integrating all the available data has the potential to improve drug potency predictions, help guide the experimental design, save costs associated with multiple expensive experiments, and ultimately lead to the identification of new drugs. Indeed, we have recently demonstrated considerable uplifts in predictive performance when incorporating multi-fidelity data through a novel, specifically-designed GNN approach . We have also demonstrated transfer learning capabilities between the different data modalities, an area that has also recently received interest in quantum machine learning .