By automatically reformulating the problem domain, constructive induction ideally overcomes the defects of the initial description. The reformulation presented here uses the Version Space primitives D(E,F), defined for any pair of examples E and F, as the set of hypotheses covering E and discriminating F. From these primitives we derive a polynomial number of M-of-N concept. Experimentally, many of these concepts turn out to be significant and consistent. A simple learning strategy thus consists of exhaustively exploring these concepts, and retaining those with sufficient quality. Tunable complexity is achieved in the MONKEI algorithm, by considering a user supplied number of primitives D(Ei,Fi), where Ei and Fi are stochastically sampled in the training set. MONKEI demonstrates good performances on some benchmark problems, and obtains outstanding results on the Predictive Toxicology Evaluation challenge.