Despite excellent performance on benchmarks, RNN de novo molecule optimization using RL can be very sample-inefficient often requiring 10s or 100s of thousands of molecules to optimize a task. For example, 163,840 molecules were sampled during HC optimization for GuacaMol benchmark tasks and 192,000 molecules were sampled during REINVENT optimization of DRD2 predicted activity (although neither study specified at which point the task was 'sufficiently' optimized, which could have been before optimization finished).