Computational chemistry can help to simplify the relationship between building blocks and self-sorting outcomes by predicting or rationalising the structure and energetics of different potential topologies, showing, for example, whether a particular combination of building blocks and topology are accessible. Structure generation and evaluation of the relative stability of different topologies can help drive or rationalise experimental efforts. However, these calculations are costly or too approximate (often neglecting solvent/ion effects and flexibility), and the thermodynamics of each step is not always enough of the picture (e.g., when kinetic traps are present). When introducing asymmetry, for example, combinatorial explosion quickly leads to an intractable number of isomers, topologies or configurations that must be studied to consider self-sorting outcomes. Therefore, new approaches to tackling these prediction and rationalisation problems are needed.