Intuitively, adding more descriptive input features to a model, such as relevant structural and electronic information, should allow for better modelling and hence better prediction performance, as observed during the preliminary studies for interpolative predicting reaction yields. The effect of adding chemical information within active learning was subsequently studied to observe how ML models perform in initial low data regimes. Four different data representations were compared: OHE, Morgan 2 fingerprints, dimensionality reduced Morgan 2 fingerprints (the first 30 principal components generated after principal component analysis (PCA) of each of the three varying reagents, giving 90 principal components in total) and the full feature set 14 (including OHE, PCA of fingerprints and all DFT features).