To ensure practicality in real-world scenarios, we designed a semi-supervised workflow that accounts for the scarcity of labels for novel targets and the complexity of ligand molecules. We have tried two strategies. Firstly, we optimize the entire test set by using the predictions from the previous round as pseudo-labels, integrating them into the model's training. This method, which shares the results across all targets using a common checkpoint, yielded promising outcomes, as evidenced by a Pearson correlation coefficient of 0.7 after two iterations as shown in Table facilitating the transition to the second approach. To address these limitations, we incorporated real value data into training and supervision. As the volume of real-value data increased, the predictive performance of the model across various targets gradually improves. When the number of real values reached six, the performance on the FEP1 dataset matched that of the FEP+ method, as shown in Figure and Table . The RMSE indicator further confirms that our method yields predictions closer to true values. We did not continue to the next iteration, considering this performance a realistic benchmark for our current modeling approach. Although continued iterations may enhance performance on the current test set, they could introduce significant bias when applied to larger datasets, restricting us to a local optimum within the vast search space. Hence, we view this method as a valuable reference, particularly suitable for specific systems.