When searching a tree to find the best leaf, complete search methods such as depth-first search and depth-bounded discrepancy search use a fixed deterministic order that may or may not be appropriate for the tree at hand. Adaptive probing is a recently-proposed stochastic method that attempts to adjust its sampling on-line to focus on areas of the tree that seem to contain good solutions. While effective on a variety of trees, adaptive probing wastes time learning basic features of the problem that are built into other algorithms, such as the fact that the heuristic is often helpful. In this paper, we investigate two simple methods for adding such prior knowledge to adaptive probing. The first simply reuses the modelearned during a previous run on a similar problem. The second uses a heuristically biased policy at the start of the search, gradually deferring to learned information in later iterations. Empirical results on two different representations of number partitioning confirm that these methods can allow adaptive probing to search efficiently from the very start of a run. However, reusing previous models seems to more frequently preserve the ability of the algorithm to adapt to the search space.