Our empirical analysis is extensive and covers several real-world datasets, various different baselines, and different problem domains and settings. The overall effectiveness and generality of the proposed approaches augur well for future applications of graph neural networks for transfer learning. More specifically, we envision impactful drug discovery applications in live high-throughput screening projects. The results in the inductive setting indicate that the massive amount of data collected during these campaigns can be transferred to predict high-fidelity activity for novel compounds that did not exist during the HTS screening campaign, without needing to synthesise them first. Furthermore, from a resource and cost utilisation perspective, such projects operate on tight schedules and a fixed budget of high-fidelity evaluations (e.g. 10,000). Here, we have shown that transfer learning is particularly useful when a low amount of high-fidelity data is available (Figure ). Transfer learning can inform and improve the effectiveness of these costly steps, resulting in much more diverse and promising active molecules as well as lower costs to the discovery processes. In hybrid experimental and in silico workflows, as few as 500 to 1,000 high-fidelity evaluations could be performed by traditional selection, with the rest of the budget invested into recommendations made with the help of transfer learning (operating in the inductive setting).