In this contribution, we investigated the SynRBL framework as an innovative approach for the rebalance of incomplete reaction entries in chemical databases. SynRBL combines a rule-based approach for carbon-balanced reactions and the MCS-based workflow for carbon-unbalanced reactions. The latter combines variants of the MCIS and MCES problem to increase the fraction of instances in which chemically correct subgraph embedding is found. For the MCS-based component, moreover, a trained feature-based machine learning model was used to estimate the prediction confidence. SynRBL was rigorously evaluated based on five meticulously curated validation datasets, encompassing a subset of the Golden dataset, the Jaworski dataset, and three variants of the USPTO 50k database. Overall, the framework achieves unprecedented accuracy, exceeding 99% on the subset of database entries that it can process successfully. These cover more than 90% of the unbalanced reactions in the datasets used for evaluation. As a by-product of the rule-based analysis, we observed that the signature O : 1, Q : 0 referring to a single oxygen is as a strong indication for an error in database entry.