Given this conceptually simple fingerprint, we show that its performance, when applied to tasks mentioned above, rivals or even surpasses that of state-of-the-art methods while using minimal non-specialised computational resources and no specialised hard-or software (see Computational Resources). The fingerprint requires an unannotated, non-atommapped reaction SMILES as input and embeds this molecular representation from reaction SMILES space into an arbitrary low dimensional binary metric space through set operations and subsequent mod hashing. We show that a k-NN classifier trained with DRFP significantly outperforms those trained on existing, non-learned fingerprints and rivals or surpasses the performance of learned fingerprints without the need for supervised learning pre-classification. Furthermore, the fingerprint can act as an unbiased benchmark for new methods. Finally, we show that this method, based on a simple set operation and hashing scheme, can outperform both deep learning-based learned fingerprints and physics-based descriptors in yield prediction tasks. We make the fingerprint creation algorithm available as a pypi package (drfp). The source code and documentation are available on GitHub (). without distinguishing between reactants and agents into an DRFP fingerprint is achieved by first extracting circular substructures of radius r (r = 3 in the above example) into two sets (blue and red circles for reactants and products, respectively). In a second step, the two sets' symmetric difference (blue and red shaded areas) is hashed using an arbitrary hash function. Finally, the resulting set is hashed into a binary vector using modular hashing.