Embedded Morgan Fingerprints for More Efficient Molecular Property Predictions With Machine Learning

High‐dimensional molecular fingerprints have become a quiet bottleneck for chemical machine learning, where bit collisions in large, chemically diverse datasets and escalating computational costs constrain model discovery. This work introduces the embedded Morgan Fingerprint (eMFP), a postprocessing scheme that compresses standard Morgan Fingerprints into continuously valued, lower‐dimensional representations governed by a compression factor. Applied to three molecular datasets of increasing size (RedDB, Non‐Fullerene Acceptors, and QM9), five regression models, and four encoding configurations under scaffold‐aware nested cross‐validation, eMFP maintains the predictive power of full‐length fingerprints for tree‐based methods at moderate compression (), while exhibiting more model‐ and setting‐dependent behavior elsewhere. Its continuous outputs pair favorably with Fourier Feature Neural Networks, where they interact more naturally with sinusoidal feature mappings than the original binary fingerprints, and consistently shorten training times per trial. Together, these results position eMFP as a simple, drop‐in route to more scalable molecular representations that retain accuracy while easing the computational burden of model development.