The commonly employed alternative to fingerprint-based rediscovery are docking-based tasks, as seen in Tartarus and the Therapeutics Data Commons. However, these tasks come with a relatively high computational cost . To strike a balance, our paper introduces a new type of benchmark: descriptor-based rediscovery of small molecules. Instead of relying on fingerprints, these tasks encode a randomly selected conformer of the target molecule using either a Ultrafast Shape Recognition with CREDO Atom Types 79 (USRCAT) or Zernike descriptor. We evaluate the fitness of generated molecules using customized similarity metrics that broadly align with Tanimoto similarity trends. These tasks prove to be more challenging and discriminative than fingerprintbased rediscovery, yet significantly less resource-intensive than docking tasks. This approach allows for statistical calculations, facilitating the thorough comparison of different generative models.