In this paper we combine all of the above, see Figure , by applying state-of-the-art Gaussian processes for molecular representations to create surrogate fitness models for GB-EPI, the previously described quality-diversity algorithm for molecular optimisation based on GB-GA. We test a range of modern acquisition functions on the surrogate fitness models to determine their ability to solve the ensemble problem of identifying the best molecule for every niche. The resulting algorithm, which we call the graphbased Bayesian illumination (GB-BI), significantly improves efficiency compared to previous approaches. The upcoming paragraphs provide an overview and technical details of how genetic algorithms, quality-diversity algorithms, and Bayesian optimization are applied, adapted, and combined to formulate GB-BI. A lightweight, open-source version of GB-BI is available for download on GitHub.