We present the technical details of the Bayesian Illumination algorithm alongside a comprehensive hyperparameter scan (considering different molecular representations) on a standardised fingerprint-based rediscovery benchmark. In addition, we introduce a novel type of benchmark, where molecules are rediscovered based on a sample of conformers and associated USRCAT or Zernike descriptors. This descriptor-based rediscovery of small molecules is both challenging for generative models and computationally affordable. To facilitate this benchmark, we provide a novel and efficient implementation of Zernike descriptors for small molecules. Finally, we also apply the Bayesian Illumination algorithm to docking based tasks. To avoid pure exploitation of docking scores and to avoid unrealistic structures for the prea Centre for Integrative Neuroplasticity, University of Oslo, N-0316 Oslo, Norway; Email: jverhell@gmail.com dicted binders, we apply stringent structural and physicochemical filters on the candidate molecules and modulate the docking scores with a factor based on the synthetic accessibility of the candidate molecule.