  Target search by active agents in rugged energy landscapes has remained a
challenge because standard enhanced sampling methods do not apply to
irreversible dynamics. We overcome this non-equilibrium rare-event problem by
developing an algorithm generalizing transition-path sampling to active
Brownian dynamics. This method is exemplified and benchmarked for a
paradigmatic two-dimensional potential with a high barrier. We find that even
in such a simple landscape the structure and kinetics of the ensemble of
transition paths change drastically in the presence of activity. Indeed, active
Brownian particles reach the target more frequently than passive Brownian
particles, following longer and counterintuitive search patterns.
