Here we present the differential reaction fingerprint (DRFP ) for reaction search and categorization as well as yield prediction. The reaction fingerprint DRFP borrows the creation of circular substructures from a molecule and the subsequent hashing of their SMILES representations from the chemical fingerprints ECFP and MHFP, respectively (see Figure and Molecular n-grams). However, as reaction SMILES consist of mul-tiple molecules in the form REACTANTS>AGENTS>PRODUCTS, three additional steps have to be introduced: (I) The agents are added to the reactants, resulting in the representation REACTANTS+AGENTS>>PRODUCTS; (II) molecules on each side of the reaction representation are processed individually, resulting in two sets of SMILES R and P ; (III) the symmetric difference of the two sets S = R P is taken, hashed using an arbitrary hash function with a sufficiently low collision probability (BLAKE2), and then further hashed into a fix-length binary vector using h(k) = k mod d, where k ∈ S, and d is the desired dimensionality of the fingerprint. Compared to the approach introduced by Schneider et al. , DRFP does not apply weights based on atom-mapping to differentiate between reactants and agents, does not require the calculation of molecular properties for the agents, and does not apply arithmetic operations on individual molecular fingerprints, such as the atom pair fingerprint, to create a reaction fingerprint.