We postulate the generic problem addressed in this work as 'what is an effective methodology to develop robust synthesis procedures based on novel synthetic methods?'. Our hypothesis is that to be effective, a methodology we are seeking should make use of a combination of (i) Bayesian active learning optimisation, which has been proven to be effective with small experimental budgets, and (ii) of an effective generation and assembly of a priori knowledge about the chemical system of interest, which will further constrain the experimental search space.