We used a fixed random seed to always generate the same candidate set, 𝐂, across all iterations of the active learning loop for a particular polymer, thickener sub-system, and also for all the sub-systems investigated. Therefore, we are always proposing experiments from the same potential design space. Our industrial partner had recommended a set of concentration bounds for us to formulate within, as detailed under step (2) in Scheme 1. If we discretised these bounds in 0.5 w/w% intervals and accounted for the surfactants selection problem, then we have approximately 360,000 combinations; hence, the size selected for 𝐂. Here, 0.5 w/w% was determined to be an appropriate step which could be comfortably resolved by our experimental procedure -automated viscous liquids handling on the Opentrons OT-2 robot . We note, we could accurately determine the composition of the prepared formulations; however, we could not always accurately dispense the target amounts specified from our DoE, especially for the highly viscous formulation ingredients. Therefore, on each iteration of the DoE, we suggested the next batch of experiments based on the actual, recorded compositions.