Effective spatial keyword queries require joint modeling of location constraints and textual semantics. Existing systems typically rely either on spatial indexes with keyword filters or on semantic embeddings, which treat the two dimensions separately and thus lead to rigid spatial boundaries and limited semantic flexibility. To better understand these limitations, we systematically evaluate six representative approaches on large-scale OpenStreetMap (OSM) data from Greater Melbourne. The results show that non-embedding methods deliver exact matches but fail to generalise semantically, while embedding-based methods broaden coverage but capture spatial context poorly. This trade-off is not inevitable. We contribute two fusion strategies, namely concatenation embedding and contrastive fusion, that map spatial coordinates and textual descriptions into a shared vector space, and we integrate them with PostgreSQL, PostGIS and pgvector for evaluation alongside traditional baselines. Experiments demonstrate that fused embeddings improve semantic recall while preserving spatial fidelity, and they remain stable as query intent shifts between location and meaning. These findings indicate that unified spatial-semantic embeddings provide a practical direction for advancing spatial keyword queries.