The Rosetta automated Monte Carlo reaction-based ligand design (RosettaAMRLD) integrates a Monte Carlo Metropolis (MCM) algorithm and reaction-driven molecule proposal to enhance structure-based de novo drug discovery. By leveraging combinatorial ultralarge libraries, RosettaAMRLD ensures synthetic accessibility, optimizing protein-ligand interactions while efficiently sampling accessible chemical space. Importantly, RosettaAMRLD can be initiated without a known binder, broadening its applicability to novel pharmaceutical targets. We applied RosettaAMRLD to three protein classes typically targeted by drugs, demonstrating its ability to generate novel, synthetically accessible ligands with active-like binding poses. Benchmark results show that RosettaAMRLD can propose diverse ligands with significantly improved docking scores compared to random sampling, and multiround iteration further enhances output quality, resulting in molecules with in silico properties exceeding those of known actives. The method's capability to explore ultralarge chemical spaces and generate novel drug-like molecules highlights its potential in early stage drug discovery.