In quantum chemistry, a family of established methods is given by the so called ∆-predictors , which are designed for the transductive setting and focus on learning additive corrections of the lowfidelity measurements when estimating the properties of interest. These approaches are defined only for quantum properties and thus on a chemical space of limited diversity (e.g. only 3, 114 unique molecules for ANI-1x/ANI-1cxx, with the heaviest atom being oxygen). Furthermore, not many high-quality datasets exists to support multi-fidelity data modelling, with the very recently introduced QMugs dataset and our own MF-PCBA collection being notable exceptions. As such, the standard choice for data, techniques, and model architectures remains single-fidelity, even for recent quantum machine learning efforts (e.g. Alchemy , QM7-X , nablaDFT ). Thus, the benefits of obtaining and holistically modelling molecular data produced at different fidelity levels remains insufficiently explored. In drug discovery, for instance, the benefits of leveraging millions of experimentally-derived molecular measurements at different fidelity levels is particularly understudied. As recent large-scale drug discovery efforts focus mainly on increased automation, reproducibility, and cost-effectiveness , the burden of making sense of the immense collections of molecular interactions falls onto computational methods. Integrative modelling of multi-fidelity data and improved molecule-level predictions could lead to cost-savings by reducing or avoiding some of the expensive wet-lab experiments, the identification of new, promising compounds, more informed experiments for ongoing projects, and hybrid wet-lab and in silico workflows. However, existing studies of real-world drug discovery data are built on a single-fidelity paradigm, even more so than in the case of quantum mechanics funnels .