  Recent successes in virtual screening have been made possible by large models
and extensive chemical libraries. However, combining these elements is
challenging: the larger the model, the more expensive it is to run, making
ultra-large libraries unfeasible. To address this, we developed a
target-agnostic, efficacy-based molecule search model, which allows us to find
structurally dissimilar molecules with similar biological activities. We used
the best practices to design fast retrieval system, based on
processor-optimized SIMD instructions, enabling us to screen the ultra-large
40B Enamine REAL library with 100\% recall rate. We extensively benchmarked our
model and several state-of-the-art models for both speed performance and
retrieval quality of novel molecules.
