Multi-step retrosynthetic route planning (MRRP) is the core task in synthetic chemistry, in which chemists recursively deconstruct a target molecule to find a set of reactants that make up the target. MRRP is challenging in that the search space is vast, and chemists are often lost in the process. Existing AI models can achieve automatic MRRP fast, but they only work on relatively simple targets, which leaves complex molecules under chemists’ expertise. To facilitate MRRP of complex molecules, we proposed a human-AI collaborative system, RetroLens, through a participatory design process. AI can contribute by two approaches: joint action and algorithm-in-the-loop. Deconstruction steps are allocated to chemists or AI based on their capabilities and AI recommends candidate revision steps to fix problems along the way. A within-subjects study (N=18) showed that chemists who used RetroLens reported faster MRRP, broader design space exploration, higher confidence in their planning, and lower cognitive load.