In response to the computational challenges posed by conventional fingerprint-based rediscovery and resource-intensive docking tasks, this paper pioneers an alternative approach: descriptorbased rediscovery of small molecules. Departing from the conventional method of determining a target molecule and calculating a fingerprint as the basis for similarity, our alternative involves sampling a random conformer and replacing the fingerprint with either a USRCAT or Zernike descriptor. To evaluate the similarity of a candidate molecule to the target, we sample multiple conformers, calculate corresponding descriptors, and apply custom, aggregating similarity metrics to this collection of descriptors. These metrics are expressly designed to align with the broad trends of the corresponding fingerprint-based Tanimoto similarities, while creating a more challenging pathway for optimisation algorithms to reach the target molecule.