However, drug discovery is not ligand discovery, and drug discovery does not only consist of optimizing a single objective in a proxy assay system . More specifically, during the lead optimization stage of a drug discovery project, multiple-parameter optimization (MPO), for parameters such as primary target activity, activity against off-targets, and also physicochemical and ADME properties such as permeability, intrinsic clearance, solubility etc. need to be optimized simultaneously , an area which has found consideration only in few computational studies . In reality the MPO process is very complicated in a drug discovery project, because the target profile could be easily changed (and even multiple times) during the course of a project, where new problems appear every step along project progress (Fig. ) . In this work, we attempted to see whether generative models can be validated retrospectively, on the one hand with public data mapped onto a pseudo-time axis, and on the other hand with real-world project data from different projects in a pharmaceutical company.