To our knowledge this compound library is the largest library considered thus far and the first to include barrier heights as a screening parameter. Notably, we show that the substituent effects on barrier heights can be estimated using ML and a very simple representation with sufficient accuracy to be useful. Despite the huge library size the screen is carried out using comparatively modest computational resources by using SQM as an intermediate step between ML and DFT calculations. SQM allows for large data sets for ML models to be constructed in a matter of days and the pre-screening tens of thousands of candidates so that DFT calculations are only performed on the most promising candi-dates. This is important because the prediction of reliable reaction energies and barrier heights requires thorough conformational searches that require significant computational resources at the DFT level. The overall methodological approach outlined in this paper is generally applicable to lead optimisation of properties involving chemical reactivity.