When expanded into discrete variables including solvents, reagents, or ligands, most of the self-optimisation algorithms mentioned above struggle with the "curse of dimensionality" (a poor scaling of performance of computational methods with the number of parameters) and inefficiencies (in predictive accuracy) of black-box optimisation algorithms. A common approach to overcome this problem has been demonstrated using molecular descriptors to map the discrete variables onto a continuous space. Parameterisation of discrete variables with relevant descriptors could increase the predictive accuracy of surrogate models and unlock new mechanistic insights. Earlier work in describing partition behaviour of solutes in solvents was demonstrated by Zissimos et al., comparing experimental Abraham descriptors with Klampt's COSMOments, often used for quantitative structure-property relationship (QSPR) and linear free energy relationships (LFER), to explain solvation phenomena for a data set of 470 compounds. The authors found a large overlap of chemical information content between the two sets, and that computationally calculated Klampt descriptors could be used to replace the experimentally calculated Abraham parameters. In comparison, Sevcik et al. used 17 descriptors as an input and was only able to predict the experimental Abraham parameter S using multiple linear regression modelling. The use of Abraham parameters for solvent selection in reactions, instead of QSPR, was demonstrated by Adjiman and colleagues in a computer-aided molecular design (CAMD) study: starting with six solvents in the training dataset and a linear regression model, the authors found nitromethane to be the best solvent for maximising reaction rate constant in different solvents for Menschutkin reaction after five iterations. Amar et al. expanded the descriptor space to 17 to optimise for high conversion (>90%) and diastereoselectivity (>60%) in asymmetric hydrogenation reaction to synthesize Brivaracetam, a new anti-epileptic drug produced by UCB. While this approach worked well for identifying ideal solvent candidates, all the continuous variables such as temperature and concentrations of substrates were kept constant during the solvent screening. Similarly, Chonghuan et al. used 15 descriptors for solvents in a Mitsunobu transesterification reaction to produce isopropyl benzoate. Using an Autoencoder for dimensionality reduction, and artificial neural networks (ANNs) as a surrogate model to design the experiments, the final surrogate model identified 1-chloropentane (93% yield) as a promising solvent for the single objective of maximising for reaction yield.