Our empirical analysis so far has assumed that transfer learning occurs between two clearly defined fidelity levels -'low' to 'high'. This is traditionally the case for the majority of drug discovery by HTS projects and QMugs, the first large-scale multi-fidelity QM dataset, with the majority of the latter being single-fidelity. However, it is interesting to consider the case where more than two fidelities are present. Although this is not a common setting, we have selected the well-known QM7b dataset which possesses HOMO and LUMO energy calculations at three different levels of theory: ZINDO, PBE0, and GW. For QM7b, as for the majority of datasets with more than two levels of fidelity there is an 'ordering' of fidelities according to their precision: ZINDO < PBE0 < GW.