K Nearest Neighbor (KNN) joins are used in many scientific domains for data analysis, and are building blocks of several well-known algorithms. KNN-joins find the KNN of all points in a dataset. However, KNN searches are computationally expensive, and many GPU KNN algorithms focus on the high-dimensional case that plainly gives a performance advantage to the GPU rather than the CPU. Consequently, in this work, we focus on a hybrid CPU/GPU approach for the low-dimensional KNN-join problem. In particular, we utilize a work queue that prioritizes computing data points in high density regions on the GPU, and low density regions on the CPU, thereby taking advantage of each architecture's relative strengths. Our approach, HybridKNN-Join, is shown to effectively augment a state-of-the-art multi-core CPU algorithm. We propose optimizations that (i) maximize GPU query throughput by assigning the GPU larger batches ofwork than the CPU; (ii) increaseworkload granularity to optimize GPU resource utilization; and, (iii) limit load imbalance between CPU and GPU architectures. Furthermore, the work queue utilized in our approach shows promise for the general purpose division of work for other hybrid CPU/GPU algorithms.