More recently, the application of Machine Learning methods and the data-driven identification of low-dimensional reaction coordinates for nucleation has emerged as a viable strategy to identify combinations of CVs that enable an effective, low-dimensional description of nucleation processes , that allows for the application of biased enhanced sampling by driving the polymorph-specific crystal nucleation. The definition of effective CVs for describing and enhancing the sampling of complex nucleation processes in solution also hinges on our ability to define order parameters that can resolve well the atomic environments that are characteristic of specific crystalline structures. While this is routinely done for crystals constructed from atomic or (spherically uniform) single particle monomers-simple colloids being an example-using bond-orientational OPs such as the Steinhardt order parameters it remains a challenge for molecular crystals. Approaches based on the calculation of generalised pair distribution functions or on the calculation of properties of the distributions of order parameters demonstrate promise but still require significant improvements in terms of computational efficiency, generalisability to molecular systems with a significant degree of conformational complexity and with hundreds of putative polymorphs . All these aspects limit their current applicability to study nucleation from solution and represent one of the most limiting bottlenecks in the current applicability of systematic nucleation studies in molecular systems.