In this section, we compare the proposed strategies with a recently proposed graph neural network architecture devised specifically for multi-fidelity learning on molecules, which we refer to as 'multi-fidelity state embedding' or MFSE for short . We evaluate MFSE on a representative selection of 3 public and 3 AstraZeneca drug discovery datasets. The datasets with the largest performance uplift when using lowfidelity information were chosen to maximise the chance of MFSE being effective. Unfortunately, MFSE is not competitive with the proposed methods on any of the selected drug discovery datasets, as can be seen from Figure . As a sanity check for the algorithm, we include an instance where MFSE is trained only on high-fidelity data ('HF only'), which performs similarly to our HF-only baseline. However, when used in the intended way ('HF + LF'), the performance does not consistently increase and even decreases in a number of instances. This inability to model both fidelities at the same time highlights the unique challenge posed by HTS drug discovery and the need for more effective transfer learning methods. At the same time, the ineffectiveness of MFSE when using both low-and high-fidelity labels shows that it is not trivial to incorporate low-fidelity information into a model even when multi-fidelity data is available.