Dense retrievers increasingly claim the ability to condition retrieval on natural-language relevance criteria beyond a short query. We identify a specific weakness in this capability: the evaluated models perform well when lexical or attribute cues distinguish the target, but become much less reliable when relevance depends on a directional relation over the same entities. We introduce SwitchIR, a controlled diagnostic that contrasts Constraint Shift and Explicit Filtering with Relational Flip, where the query and document pair remain fixed while the requested relation direction reverses. Across representative embedding and late-interaction retrievers, the primary joint metric is high on the two cue-available controls and consistently low on Relational Flip; the same ordering persists in an LLM-assisted, manually reviewed realistic variant. An exact margin decomposition shows that both directions are correct only when the direction-sensitive query--document interaction exceeds the direction-invariant pair preference. Empirically, direction-sensitive changes are common, but their aligned interaction is usually too weak to overcome that preference. Diagnostic fine-tuning shows that models can learn relational behavior that generalizes across relation families, with stronger generalization in the larger model.

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