In this research we asked the question "Can a generative model trained on early-stage project compounds generate middle/late-stage compounds de novo?" To this end, we used experimental data from five public and six in-house project datasets as reference datasets and investigated the ability of different REINVENT protocols (i.e., focused learning, reinforcement learning, inception and diversity filter) to model the elapsed time of a synthetic expansion following hit identification. In all protocols, we found that late-stage compound rediscovery was very small for all datasets, however, rediscovery and aSNN to late-stage compounds was greater for public projects than that for in-house projects. Upon further investigation, the underlying aSNN between early-and middle/late-stage compounds in public projects was higher between active compounds than inactive compounds; however, for in-house projects the generative models in the drug discovery process is that we require seeds of promising compounds in the training dataset; active learning might be a stepwise approach here. At the same time, we have shown that the generative model recovers very few middle/late-stage compounds from real-world drug discovery projects, highlighting the fundamental difference between human and automated design, as well as the difference between single-objective and multi-parameter optimization, with the latter being the norm in real-world drug discovery projects.