The prevalent assumption in computer-assisted synthesis planning has been to rely on the wealth of reaction data and on the consideration of this vast knowledge base at every stage of route planning. Yet even if equipped with all requisite knowledge of individual reaction transforms and state-of-the-art search algorithms, the existing programs struggle when confronted with advanced targets, such as the complex peptides this work considers. By contrast, when the searches are constrained by hierarchical logic, dictating which subsets of reactions to apply at different stages of synthesis planning, these algorithms are able to plan, within minutes, complete routes to clinically relevant targets as complex as vancomycin and as large as semaglutide. Despite not being trained on any literature precedents, the routes designed by the algorithm mimic the strategies used by human experts. The hierarchical planning we describe incorporates protecting-group strategies and realistic pathway pricing and can be performed in solid-state or solution modes, in the latter case using either C-to-N or N-to-C peptide extension strategies.