  We focus on low-dimensional non-metric search, where tree-based approaches
permit efficient and accurate retrieval while having short indexing time. These
methods rely on space partitioning and require a pruning rule to avoid visiting
unpromising parts. We consider two known data-driven approaches to extend these
rules to non-metric spaces: TriGen and a piece-wise linear approximation of the
pruning rule. We propose and evaluate two adaptations of TriGen to
non-symmetric similarities (TriGen does not support non-symmetric distances).
We also evaluate a hybrid of TriGen and the piece-wise linear approximation
pruning. We find that this hybrid approach is often more effective than either
of the pruning rules. We make our software publicly available.
