  The need for efficient computational screening of molecular candidates that
possess desired properties frequently arises in various scientific and
engineering problems, including drug discovery and materials design. However,
the large size of the search space containing the candidates and the
substantial computational cost of high-fidelity property prediction models
makes screening practically challenging. In this work, we propose a general
framework for constructing and optimizing a virtual screening (HTVS) pipeline
that consists of multi-fidelity models. The central idea is to optimally
allocate the computational resources to models with varying costs and accuracy
to optimize the return-on-computational-investment (ROCI). Based on both
simulated as well as real data, we demonstrate that the proposed optimal HTVS
framework can significantly accelerate screening virtually without any
degradation in terms of accuracy. Furthermore, it enables an adaptive
operational strategy for HTVS, where one can trade accuracy for efficiency.
