For efficient use of experimental resources, it is beneficial to have good predictive models operating on sparse datasets and guiding the high-fidelity evaluations relative to properties of interest. The latter is the most expensive part of the funnel and to efficiently support it, we consider it in a transfer learning setting designed to leverage low-fidelity observations to improve the effectiveness of predictive models on sparse and high-fidelity experimental data. In drug discovery applications of this setup, low-fidelity measurements can be seen as ground truth values that have been corrupted by noise, experimental or reading artefacts, or are simply performed using less precise but cheaper experiments. For quantum mechanics simulations, low-fidelity data typically corresponds to approximations or truncations of more complex and computationally-expensive calculations, such that the low-and high-fidelity labels are closely related. Thus, it is natural to expect that incorporating low-fidelity measurements as an input feature into a high-fidelity model typically improves the performance on sparse tasks relative to predictors learnt using the high-fidelity data alone. Despite its apparent simplicity, even in the transductive learning setting (i.e. low-fidelity and high-fidelity labels are available for all data points), it is not trivial to define an adequate workflow that jointly uses both low-and high-fidelity labels. For instance, devising an endto-end training scheme with low-and high-fidelity labels as part of the same model can be challenging for drug discovery applications, where the disparity between the numbers of respective observations is larger than two orders of magnitude (e.g. the number of high-fidelity observations can be over 500 times lower than that of the low-fidelity ones). Previous work has successfully applied multi-fidelity learning on several problems , but as we show in Results (Section 2.5) that approach is unfortunately not effective in drug discovery. While successfully exploiting multi-fidelity data in the transductive setting is valuable on its own, virtually all high-throughput screening steps in drug discovery are followed by experiments generating high-fidelity measurements for molecules that were not part of the original screening cascade, i.e. lacking low-fidelity labels. Devising an in silico model of the low-fidelity portion of the screening cascade is thus highly desirable, as it enables the generation of low-fidelity representations (e.g. labels) for arbitrary molecules that were not part of the original funnel. This inductive learning capability is crucial, as drug discovery requires making predictions about molecules that have not been made yet, and therefore models that rely on transductive information (i.e. measured low fidelity labels) are generally inapplicable. Even the highest throughput assays require that the molecules of interest must be synthesised first.