Abstract
<jats:p>Algorithmic molecular fingerprints remain attractive for high-throughput chemoinformatics because they are fast, discrete, and easy to integrate into screening, similarity search, and machine-learning workflows. However, widely used circular fingerprints such as ECFP rely on conventional molecular graph representations that capture stereochemistry incompletely and may distinguish chemically equivalent resonance forms through localized atom charges and bond-order-dependent encodings. Here, we introduce the Stereochemistry-Aware Circular Algorithm (SACA) and the derived Stereochemistry-Aware Circular Fingerprint (SACFP), a stereo chemically informed, resonance-invariant extension of circular graph refinement built on the StereoMolGraph representation.[Papusha and Leonhard, J. Chem. Inf. Model, 2026, 66, 7, 3830] In this framework, stereo chemistry is encoded through local stereodescriptors grounded in group theory, allowing atom-centered and bond-centered stereochemical information to be incorporated directly while avoiding artificial distinctions be tween resonance structures. We show that SACA yields locally consistent atom invariants for stereoisomers, preserves equivalence across resonance forms, and discriminates challenging stereochemical cases, including delocalized radicals and metal complexes. Compared with ECFP on stereoisomer datasets, SACFP produces less over-discriminating atom identifiers and a more reliable mapping between subgraphs and fingerprints. Evaluation on 1.4 million ChEMBL small molecules shows a low color-refinement-hash collision rate of 0.1%, with remaining ambiguities resolvable by exact StereoMolGraph isomorphism. We showcase how its mathe matical properties make it suitable for substructure search on large-scale datasets.</jats:p>