  Multi-agent path finding (MAPF) is a task of finding non-conflicting paths
connecting agents' specified initial and goal positions in a shared
environment. We focus on compilation-based solvers in which the MAPF problem is
expressed in a different well established formalism such as mixed-integer
linear programming (MILP), Boolean satisfiability (SAT), or constraint
programming (CP). As the target solvers for these formalisms act as black-boxes
it is challenging to integrate MAPF specific heuristics in the MAPF
compilation-based solvers. We show in this work how the build a MAPF encoding
for the target SAT solver in which domain specific heuristic knowledge is
reflected. The heuristic knowledge is transferred to the SAT solver by
selecting candidate paths for each agent and by constructing the encoding only
for these candidate paths instead of constructing the encoding for all possible
paths for an agent. The conducted experiments show that heuristically guided
compilation outperforms the vanilla variants of the SAT-based MAPF solver.
