The size of the corpus of chemical reaction data obtained from public, proprietary, and licensed sources has progressively grown over the years, matched by an increasing demand for extracting more value from chemical experiments. Scientists use computational tools and smart search methods to navigate this data effectively. For example, the Network of Organic Chemistry (NOC) approach converts individual chemical reactions into a graphlike object, allowing for graph-based searches and the discovery of new synthetic routes. Additionally, the application of predictive modeling has led to the development of Computer-Aided Synthesis Planning (CASP) tools, which provide actionable insight in the form of synthetic plans to molecular targets. To address the need for common frameworks across multiple CASP/NOC tools, including the manual input of experts, we extended the Python toolkit LinChemIn 24 with new functionalities. By merging digital synthetic routes from multiple sources (both CASP platforms/models and input from experts), LinChemIn identifies novel connections between reactions, even ones not initially present in the initial inputs. This approach offers multiple alternative paths to strategic intermediates, enhancing the decision-making process and, ultimately, improving synthetic route design. This application note provides an overview of the concepts introduced in LinChemIn's new module and outlines its usage. It showcases the route operations enabled by the toolkit through a practical case study. The note concludes with a glimpse of the future development roadmap. The source code of LinChemIn is freely available on GitHub at and it is open to feedback and contributions from the community.