Abstract Global optimization problems with so-called ‘rough’ or rugged objective function landscapes are studied. These problems often have many, many stationary points and show considerable differences between small and large-scale geometry. A novel multi-scale global optimization algorithm for solving ‘rough’ objective functions, based on the alternate use of terrain methods and new funneling algorithms, is presented. Small-scale information is gathered using a terrain optimization methodology while funneling algorithms are used to guide the overall optimization calculations and to make ‘large’ moves within the feasible region. A molecular modeling example is used to clearly illustrate that the proposed methodology is capable of finding a global minimum without calculating all stationary points and can lead to significant reductions in computational work.