Automated bug localization methods can accelerate the process of programmers locating complex software system defects using bug reports. Early researchers treated bug localization as a retrieval task, constructing defect features by analyzing bug reports and related code, and applying information retrieval techniques for bug localization. With the development of deep learning, bug localization methods utilizing deep model features have also achieved certain effectiveness. Nevertheless, existing deep learning-based bug localization research methods suffer from experimental search space mismatching real-world scenarios due to the high time and resource costs of deep model training. These research methods do not consider all the files in the project as the search space during testing; they only search for code related to marked defects, such as the DNNLOC method, DreamLoc method, and DeepLocator method. This approach is inconsistent with the actual search scenario for programmers to localize real bug. In order to simulate the real-world scenario of bug localization, this study proposes the TosLoc method, which combines information retrieval and deep model features for bug localization. Firstly, information retrieval is employed to retrieve all source codes of real projects to ensure comprehensive utilization of existing features. Subsequently, deep models are utilized to extract semantics from source codes and bug reports. The TosLoc method achieves rapid localization of all code in a single project through two-stage retrieval. Experimental results conducted on four popular Java projects demonstrate that the proposed TosLoc method outperforms existing benchmark methods in terms of retrieval speed and accuracy. Compared to the best method called DreamLoc, the TosLoc method achieves an average MRR improvement of 2.5% and an average MAP improvement of 6.0% while only requiring 35% of the retrieval time of the DreamLoc method.