One key decision for heuristic search algorithms is how tobalance exploration and exploitation. In classical planning,novelty search has come out as the most successful approachin this respect. The idea is to favor states that contain previ-ously unseen facts when searching for a plan. This is done bymaintaining a record of the tuples of facts observed in previ-ous states. Then the novelty of a state is the size of the small-est previously unseen tuple. The most successful version ofnovelty search is best-first width search (BFWS), which com-bines novelty measures with heuristic estimates. An orthog-onal approach to balance exploration-exploitation is to useseveral open-lists. These open-lists are ordered using differ-ent heuristic estimates, which diversify the information usedin the search. The search algorithm then alternates betweenthese open-lists, trying to exploit these different estimates.This is the approach used by LAMA, a classical planner that,a decade after its release, is still considered state-of-the-artin agile planning. In this paper, we study how to combineLAMA and BFWS. We show that simply adding the strongestopen-list used in BFWS to LAMA harms performance. How-ever, we show that combining only parts of each planner leadsto a new state-of-the-art agile planner.