Due to practical limits on computational resources, solid-state density functional theory with dispersion corrections (DFT-D) is the most accurate modelling framework that is routinely applicable in the context of CSP methodologies. In many cases, DFT-D has been shown to reliably predict polymorph energy differences , despite well-known issues in some cases with conformational polymorphism and delocalisation errors . Even so, the precision of DFT-D calculations still comes at a significant computational cost which often makes it prohibitive to apply DFT-D to more than a small number of structures in the CSP search. As such, the use of DFT-D is often restricted to providing a final ranking of polymorphs in the later stages of a hierarchical CSP study, or to perform a posteriori free energy calculations . In the earlier stages of the CSP workflow, force-field models are usually used due to their comparatively low computational cost. Transferable force-field models typically used in molecular dynamics applications were common in the pioneering days of CSP , but models that incorporate some form of system-specific customisation are increasingly being used . Tailor-made force-fields (TMFF) have been successfully applied in CSP methodologies and resemble a typical molecular dynamics force-field, but the model must be reparameterized from solid-state DFT-D calculations for each system to obtain a customised empirical model of molecular interactions . Work has also been carried out on the development of bespoke potentials using SAPT(DFT) where all terms in the classical model are derived from ab initio calculations on the system of interest . For instance, in SAPT(DFT) derived models, parameters are either tailored directly to the potential energy surface of a molecule from theoretical calculations on hundreds or thousands of dimer interactions , or are determined ab initio from the properties of the molecular charge density . More recently, machine-learning based methods have gained increased interest within CSP for molecular systems. However, limitations on computational costs and data availability hinder the viability of purely machine-learned models. Instead, machine-learning models have been implemented as corrections over non-machine learning CSP methods, reducing the volume of training data required to a manageable level . While the efficacy of these methods have been demonstrated in relatively small and rigid molecules, their effectiveness on more flexible molecules has yet to be reported. Another class of models, which we henceforth refer to as hybrid ab initio/empirical force-field (HAIEFF) models, has been well-established within common CSP methodologies . In their most basic form, HAIEFF models partition the crystal lattice energy (U latt ) into independent energetic contributions that include the cost of deforming the molecules in the crystal from their gas phase conformation (∆U intra ), intermolecular electrostatic interactions (U elec ), and finally a residual term (U res ):