In decision tree learning, attribute selection is usually based on a greedy local splitting criterion. More extensive search is time consuming and usually does not benefit prediction accuracy as much as one would hope. It has even been claimed that look-ahead would be harmful in decision tree learning. The pathological effects of look-ahead have been studied analytically in the context of game trees. These results and their connection to decision tree learning are considered. A computationally efficient splitting algorithm for numerical domains is presented, which is based on ideas from search tree look-ahead. In empirical tests, the algorithm performs in a promising manner.