GPU query backends achieve high throughput on analytical work- loads through massive parallelism, but lack indexing support that accelerates selective queries in CPU databases. Existing GPU index implementations face three limitations: (1) supporting conjunctive predicates only, (2) materializing intermediate results between in- dex access and query execution, and (3) assuming query boundaries align with pre-built index bins. We present a fused bitmap indexing approach that addresses these limitations. We introduce virtual query program that exe- cutes arbitrary boolean predicates with low overhead. We fuse index access with subsequent column lookups, joins, and aggre- gation, keeping intermediate results in registers and eliminating global-memory round-trips. To handle misaligned query bound- aries, we propose GPU friendly candidate checking that tracks three-valued row states (certain-in, certain-out, uncertain) through in-register boolean operations and verifies only the necessary can- didates, without accessing global memory. On Star Schema Benchmark SF=140 with RTX 5090 D, our fused bitmap index achieves up to 6.9× geometric-mean speed over our optimized non-indexed baseline built upon the Crystal GPU data- base query backend (Dense layout), and 4.3× with practical Sparse layout using less memory. Compared to current best compressed GPU bitmap implementation under perfect bin alignment (best case), our Sparse layout achieves 1.4× speed end-to-end. We show that generic elementwise-style GPU fusion achieves only 1.34× speed, while our pipeline reaches 3.19× with 0.8% overhead versus dedicated compile-time kernels. Results on an NVIDIA H800 server GPU show the approach remains stable across GPU architectures, with smaller but still consistent fusion benefits on server GPUs.