The ANI-1x dataset was constructed utilizing active learning to broadly cover the space of conformations of neutral druglike molecules and utilizes the ωB97X functional in conjunction with a rather modest double zeta quality basis set, 6-31G(d). A subset of this data was subsequently labeled with a much higher coupled cluster level of theory combined with a near complete basis set (DLPNO-CCSD(T)/CBS) and was utilized to demonstrate a transfer learning protocol. The authors of that work show that this procedure gives significantly improved results when testing on challenging reaction energies of isomers and reproduction of torsional energy profiles in reference to high levels of theory. In Sec. 3.1 we show that multitask training on these two datasets is a viable alternative with results nearly indistinguishable from those of the original transfer learning protocol.