Generalized bi-prediction (GBi) is included in BMS-2.0 to improve the motion compensated prediction. In GBi, bi-prediction is generated using a weighting parameter index signaled at the CU level. In order for the encoder to select the optimal GBi weight index, multiple sets of weights need to be evaluated based on rate-distortion (RD) cost, which increases the encoding complexity. In this contribution, five encoder speed-up methods and one GBi encoder bug fix are proposed to reduce the encoding complexity while keeping GBi’s coding efficiency. The encoder speed-up methods include: (1) skipping affine motion estimation for some GBi weights conditionally; (2) reducing the number of weights for RD cost checking for low-delay pictures in the encoding for 1-pel and 4-pel MVD precision; (3) conditionally skipping bi-prediction search when reference pictures of two lists are the same; (4) skipping RD cost checking for unequal GBi weights based on temporal layer and the POC distance between reference picture and current picture; (5) changing floating calculation to fixed-point calculation for unequal GBi weights. The encoder speed-up can achieve 6% encoding time reduction with about 0.04% loss. The bug fix is related to the combination of GBi and 6-parameter affine model. Experimental results show that compared to VTM-2.1 anchor, with the proposed GBi encoder speed-up and encoder bug fix achieve average {Y, U, V} BD-rate savings of {-0.73%, -0.93%, -0.89%}, {-0.32%, -0.32%, -0.42%} for RA and LD, respectively.