Of the many novel transformations developed over the last decade, we were attracted to a process involving a photocatalyzed multi-component reaction to generate C(sp3)-rich tertiary alkylamines from readily available starting materials, and we selected to demonstrate out methodology on this case study. A proposed mechanism for this transformation is shown in Scheme 1. The developed workflow is shown schematically in Figure . In the workflow, a machine learning-driven optimisation the amine synthesis was demonstrated with six continuous variables and 20 solvents, using a semi-automated continuous flow setup. Starting with a library of 115 solvents, the workflow includes multiple steps of a priori knowledge generation (e.g., solubility predictions and measurements, UV-Vis and actinometry studies) to narrow down the space of discrete choices in this reaction system. Once prior knowledge is assembled, we then used the NEMO algorithm to identify and populate the Pareto front for the two reaction objectives -yield and cost -in a semi-automated continuous flow optimisation campaign. While demonstrated on a specific example of a challenging-to-scale synthetic chemical procedure, the approach of iterative identification of the relevant reaction context that aids and constrains active learning reaction optimisation is generic. It is our further goal to develop an automated methodology for prior knowledge generation, which will be described elsewhere.