We developed a weighted-space filling design for liquid formulations built on restricting MaxProQQ designs to stable predicted regions of difficult-to-formulate sub-systems, where a sub-system looked at a fixed polymer and thickener combination. We produced a chemically interpretable featurisation by considering the functional groups present in our surfactant ingredients. Future work would be to extend the molecular representation also to the macromolecular ingredients. We successfully trained highly predictive phase stability classifiers for three difficult-to-formulate sub-systems. We used these classifiers to guide our experiments to regions of stability. For the other five sub-systems, we used purely space-filling designs to better cover the entire design space. However, we discuss through the use of a tuneable phase stability cut-off in our ML-guided DoE method how we balanced exploration of our design space with exploitation towards feasible regions for the difficult-to-formulate sub-systems too. Therefore, the overall spread and coverage of our designs show satisfactory properties -a relatively uniform spread of ingredients and coverage of the entire concentration ranges we were interested in. The resulting experimental dataset from this work, therefore, represents the full design space from the set of available ingredients and suggested concentrations from our industry partner, which can now be used to develop property prediction models to accelerate formulation design.