In this paper, we use the potential of the near-data parallel computing presented in the Hybrid Memory Cube (HMC) to process near-data query filters and mitigate the data movement through the memory hierarchy up to the x86 processor. In particular, we present a set of extensions to the HMC Instruction Set Architecture (ISA) to filter data in-memory. Our near-data filters support vector instructions and solve data and control dependencies internally in the memory: internal register bank and branch-less evaluation of data filters transform control-flow dependencies into data-flow dependencies (i.e., predicated execution). We implemented the near-data filters in the select scan operator and we discuss preliminary results for projection and join. Our experiments running the select scan achieve performance improvements of up to 5.64× with an average reduction of 80% in energy consumption when e