Strategies for the C-N cross coupling benchmark need to be able to select continuous variables (temperature, base equivalents, residence time) and categorical variables (the base and catalyst). Only SOBO and GRYFFIN can inherently work with categorical variables. To overcome this challenge, we calculate a set of descriptors or properties for the catalyst and base that transform the categorical variables into continuous variables. Previous work has shown that descriptors such as melting and boiling point or those from thermodynamic programs can help the optimisation. We use the first two of five σ-moments from computational fluid thermodynamics program COSMO-RS as these act as universal descriptors for any molecule. We would prefer to use all five σ-moments, but we found that it is difficult to optimise in the resulting fourteen-dimensional input space. Using only the first two σ-moments for the catalyst and base makes it a seven-dimensional input space (three continuous variables and two categorical variables with two descriptors each). Note that DRO is not included because we empirically found that pre-training the policy on input spaces greater than six-dimensions was very slow. Since the C-N benchmark is also a multi-objective problem, we use the same transforms as in S N Ar benchmark.