The in-house dataset was retrieved from TEIJIN Pharma Ltd's Database, and we selected 6 projects (A to F) which have more than 1,000 bioactivity datapoints of pXC50 values. The date of completed synthesis for in-house compounds was recorded in the TEIJIN database for a given project. Consequently, being different from public dataset, we directly used the date for the time-series of the in-house dataset. An additional difference from the public dataset was that we know there was at least one additional property to be improved for each project that was not on-target activity, such as metabolism, physicochemical properties, etc., and this can even change at different timelines of the project . Hence a second objective of using this dataset, apart from benchmarking generative models, was to evaluate to what extent the structures generated by de novo generative models actually follow the optimization trajectory across a set of real-world drug discovery projects. Regarding data classification along the bioactivity axis, for projects A, B, C, D, the setting of activity classification was the same as public dataset. However, for projects of E and F, given the bioactivity distribution in those cases, thresholds for activity classes have been set to less than 7 for "low", over 7 to less than 8 for "middle", over 8 to less than 9 for "high", over 9 for "ultra-high". As for the classification of stages of in-house projects, 500 or 1,000 was selected based on the progression of bioactivity in order to evenly split activity groups.