Engineering chemical entities to modify how pharmaceutical targets function, as it is done in drug design, requires a good understanding of molecular recognition and binding. In this context, the limitations of statically describing bimolecular recognition, as done in docking/scoring, call for insightful and efficient dynamical investigations. On the experimental side, the characterization of dynamical binding processes is still in its infancy. Thus, computer simulations, particularly molecular dynamics (MD), are compelled to play a prominent role, allowing a deeper comprehension of the binding process and its causes and thus a more informed compound selection, making more significant the computational contribution to drug discovery (Carlson, H. A. Curr. Opin. Chem. Biol. 2002, 6, 447-452). Unfortunately, MD-based approaches cannot yet describe complex events without incurring prohibitive time and computational costs. Here, we present a new method for fully and dynamically simulating drug-target-complex formations, tested against a real world and pharmaceutically relevant benchmark set. The method, based on an adaptive, electrostatics-inspired bias, envisions a campaign of trivially parallel short MD simulations and a strategy to identify a near native binding pose from the sampled configurations. At an affordable computational cost, this method provided predictions of good accuracy also when the starting protein conformation was different from that of the crystal complex, a known hurdle for traditional molecular docking (Lexa, K. W.; Carlson, H. A. Q. Rev. Biophys. 2012, 45, 301-343). Moreover, along the observed binding routes, it identified some key features also found by much more computationally expensive plain-MD simulations. Overall, this methodology represents significant progress in the description of binding phenomena.