Tag search, which is to find a particular set of tags in a radio frequency identification (RFID) system, is a key service in such important Internet-of-Things applications as inventory management. When the system scale is large with a massive number of tags, deterministic search can be prohibitively expensive, and probabilistic search has been advocated, seeking a balance between reliability and time efficiency. Given a failure probability $\frac {1}{\mathcal {O}(K)}$ , where $K$ is the number of tags, state-of-the-art solutions have achieved a time cost of $\mathcal {O}(K \log K)$ through multi-round hashing and verification. Further improvement, however, faces a critical bottleneck of repetitively verifying each individual target tag in each round. In this paper, we present an efficient tree-based tag search (TTS) that approaches $\mathcal {O}(K)$ through batched verification. The key novelty of TTS is to smartly hash multiple tags into each internal tree node and adaptively control the node degrees. It conducts bottom–up search to verify tags group by group with the number of groups decreasing rapidly. Furthermore, we design an enhanced tag search scheme, referred to as TTS+, to overcome the negative impact of asymmetric tag set sizes on time efficiency of TTS. TTS+ first rules out partial ineligible tags with a filtering vector and feeds the shrunk tag sets into TTS. We derive the optimal hash code length and node degrees in TTS to accommodate hash collisions and the optimal filtering vector size to minimize the time cost of TTS+. The superiority of TTS and TTS+ over the state-of-the-art solution is demonstrated through both theoretical analysis and extensive simulations. Specifically, as reliability demand on scales, the time efficiency of TTS+ reaches nearly 2 times at most that of TTS.