Within the last few years several papers have been published on using machine learning (ML) to generate molecules with the aim of optimizing their properties. While the performance of these generative models have been impressive, they are usually not compared to more traditional approaches such as genetic algorithms (GAs), and recent work by Tsuda and co-workers suggest that non-ML approaches may be competitive. The lack of comparison is perhaps in part due to the fact that there are no free or open source versions of these GA implementation available (ACSESS uses the proprietary OpenEye cheminformatics toolkits).