As discussed in the main text, the chosen method of combining scores was by multiplication of normalized reaction scores. Figure shows two experiment GA runs where with different scoring objectives. The generations with addition of reaction energies is observed to be too flexible in both dimensions. Optimizing towards one reaction energy can significantly improve the score. As the first reduction is a heavily exergonic reaction for the schrock cycle, the GA could easily optimize towards ligands that improved mostly on this reduction reaction. The population moves almost exclusively upwards along the axis of reduction as generations increase. On the other hand, we have the minimization of the highest reaction energy which leads to a clear improvement of the protonation energy on the x-axis. The first reduction is usually highly exothermic, effectively meaning that this objective was likely only optimizing on the protonation energy. Figure shows the result of a GA run where the starting population was comprised of the top 10 molecules from five different 50 generation runs. The population score improvement is noticeable from generation 1 to 10, but quickly converges and minor improvement is observed from generation 10 to 20. The improvement gained from the extra generations is minor and computation time could be better spent using a variety of non optimized starting populations.