High-throughput molecular docking is a data-driven simulation methodology to estimate millions of molecules' position and interaction strength (ligands) when interacting with a given protein site. Because of its data-driven nature, the high-throughput molecular docking performance depends on how fast we can ingest data into the processing pipeline and how efficiently we can write molecular docking results to a shared file. This work characterizes the I/O performance of a high-performance, high-throughput molecular docking application, called Docker-HT, running on a supercomputer up to 512 computing nodes with two different parallel I/O configurations. We show that a tuned I/O configuration can improve the overall parallel efficiency from 71% to 90% on 512 nodes and identify and solve a performance degradation observed when running on 16 and 32 nodes.