Overall, we present results that introduce and back the value of considering molecular set representation learning as an additional important branch of machine learning in computational chemistry and cheminformatics, as it provides a relaxation of the explicit molecular graph topology used in graph representation learning, which we showed to improve and extend capabilities across a wide range of tasks. The results of our set-based and -enhanced methods on the OCELOT chromophore and PDBbind data sets also support our initial hypothesis that a more relaxed definition of molecular topologies enables the neural network to learn a more meaningful representation of molecules involving conjugated or transient bonds.