An attractive feature of physics-based MLIPs such as AIMNet2-Pd is their ability to describe chemical systems that differ from the training dataset due to their ability to abstractly learn the governing chemical physics from reference DFT calculations. To demonstrate the generality and transferability of AIMNet2-Pd, we considered two related but distinct systems: (a) the widely used bidentate bisphosphine ligands and (b) dinuclear Pd(I) complexes that have recently found significant interest as catalysts with complementary reactivity and selectivity in a variety of Pd catalyzed reactions. Example applications of these catalysts include the synthesis of benzofurans and asymmetric allylic alkylation. As AIMNet2-Pd was trained only on monophosphine Pd (0) and Pd (II) complexes, it is worth examining the extent to which AIMNet2-Pd can be applied to these unseen organopalladium compounds.