Name lookup is a key operation in fundamental building blocks of information-centric networking, content delivery network, as well as the user plane function of 5G core network. It is required to deal with the longest prefix matching with a large-scale rule table, and thus confronts with serious challenges on lookup speed, update overhead and memory cost. In this paper, we design the hybrid counting Bloom filter (HyCBF) that maintains name prefixes and prefix markers within a single counting Bloom filter, while keeping them logically separated. This offers more guidance information without additional memory cost and time overhead. On this basis, we propose a HyCBF-assisted binary search (HyBS) scheme for efficient name lookup. Further, to mitigate the performance loss caused by backtracking operation during the binary search, we associate each unit of the HyCBF with a feature bitmap so as to reduce its false positive rate. Our extensive evaluations show that HyBS outperforms the state-of-the-art approaches in terms of lookup performance, update speed, as well as memory efficiency. In addition, we integrate HyBS into the vector packet processing (VPP) platform to evaluate its performance in terms of system implementation. The experimental results clearly demonstrate their potential to build a high throughput and scalable name lookup engine.